An inter-comparison of PM10 source apportionment

Environ Sci Pollut Res DOI 10.1007/s11356-016-6599-z

RESEARCH ARTICLE

An inter-comparison of PM10 source apportionment using PCA and PMF receptor models in three European sites Daniela Cesari 1 & F. Amato 2 & M. Pandolfi 2 & A. Alastuey 2 & X. Querol 2 & D. Contini 1

Received: 6 October 2015 / Accepted: 30 March 2016 # Springer-Verlag Berlin Heidelberg 2016

Abstract Source apportionment of aerosol is an important approach to investigate aerosol formation and transformation processes as well as to assess appropriate mitigation strategies and to investigate causes of non-compliance with air quality standards (Directive 2008/50/CE). Receptor models (RMs) based on chemical composition of aerosol measured at specific sites are a useful, and widely used, tool to perform source apportionment. However, an analysis of available studies in the scientific literature reveals heterogeneities in the approaches used, in terms of Bworking variables^ such as the number of samples in the dataset and the number of chemical species used as well as in the modeling tools used. In this work, an inter-comparison of PM10 source apportionment results obtained at three European measurement sites is presented, using two receptor models: principal component analysis coupled with multi-linear regression analysis (PCA-MLRA) and positive matrix factorization (PMF). The intercomparison focuses on source identification, quantification of source contribution to PM10, robustness of the results, and how these are influenced by the number of chemical species available in the datasets. Results show very similar component/factor profiles identified by PCA and PMF, with some discrepancies in the number of factors. The PMF model

appears to be more suitable to separate secondary sulfate and secondary nitrate with respect to PCA at least in the datasets analyzed. Further, some difficulties have been observed with PCA in separating industrial and heavy oil combustion contributions. Commonly at all sites, the crustal contributions found with PCA were larger than those found with PMF, and the secondary inorganic aerosol contributions found by PCA were lower than those found by PMF. Site-dependent differences were also observed for traffic and marine contributions. The inter-comparison of source apportionment performed on complete datasets (using the full range of available chemical species) and incomplete datasets (with reduced number of chemical species) allowed to investigate the sensitivity of source apportionment (SA) results to the working variables used in the RMs. Results show that, at both sites, the profiles and the contributions of the different sources calculated with PMF are comparable within the estimated uncertainties indicating a good stability and robustness of PMF results. In contrast, PCA outputs are more sensitive to the chemical species present in the datasets. In PCA, the crustal contributions are higher in the incomplete datasets and the traffic contributions are significantly lower for incomplete datasets.

Responsible editor: Gerhard Lammel

Keywords Receptor models . Principal component analysis . Positive matrix factorization . Aerosol sources

* Daniela Cesari [email protected]

Introduction

1

Institute of Atmospheric Sciences and Climate, National Research Council (ISAC-CNR), Str. Prv. Lecce-Monteroni km 1.2, 73100 Lecce, Italy

2

Institute of Environmental Assessment and Water Research, Spanish Research Council (IDÆA-CSIC), c/Jordi Girona 18-26, 08034 Barcelona, Spain

Concentrations of airborne particulate matter (PM) and their attribution to specific sources represent an important and actual research topic. The possibility to discriminate between different types of sources and between natural and anthropogenic contribution is of outmost importance, especially in areas having legislation threshold exceedances, to plan

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efficient remediation and mitigation strategies. In addition, the Directive 2008/50/CE (EC 2008) states that if natural aerosol contributions to atmospheric pollutants in ambient air can be determined with sufficient certainty, and where exceedances of a legislation limit are due entirely (or in part) to these natural contributions, these may be deducted when assessing compliance with air quality standards. This is of particular relevance in those countries where the natural contributions could often influence PM levels, like, for example, the Mediterranean region. Advection of Saharan dust can significantly increase aerosol levels in the Mediterranean area, and the contribution of this source is larger, on average, moving from North to South Europe and from west to east (Querol et al. 2009; Pey et al. 2013). The advection of Saharan dust is particularly frequent in South Italy (Contini et al. 2014a), and it is often accompanied to relevant contributions of marine aerosol in the coarse fraction of aerosol (Contini et al. 2014b). Therefore, it is important to discriminate between different types of sources (local or not local) and then between the natural and anthropogenic ones to design effective mitigation strategies. Source apportionment (SA), applied to atmospheric aerosol, is the practice of deriving information about aerosol sources and their contribution to measured ambient PM concentrations. This task can be accomplished using direct methods (source-oriented models) or inverse method (receptor models (RMs)). RMs have been extensively used to estimate the contribution of emission sources to atmospheric PM in specific sites (Bove et al. 2014; Belis et al. 2013; Argyropoulos and Samara 2011; Alleman et al. 2010; Viana et al. 2008a; Koçak et al. 2009; Mazzei and Prati 2009; Nicolás et al. 2008; Pandolfi et al. 2008; Karar and Gupta 2007; Hopke et al. 2006; Watson and Chow 2005; Rodrıguez et al. 2002). There is a large variety of receptor models used in the last 30 years, which are based on different mathematical approaches, but, in the period 2000–2012, a shift from principal component analysis and classical factor analysis to positive matrix factorization was observed (Belis et al. 2013). The analysis of the application of RMs to atmospheric PM reveals a certain variability in the proposed analysis, particularly in terms of the Bworking variables^ used (number of samples present in the dataset and number of chemical species employed). The chemical species included in the SA play an important role in the identification of sources, given the difficulties in the characterization of sources with similar chemical profiles. For example, particles of crustal origin in residential/ urban areas are often due to the simultaneous presence of a local contribution (such as re-suspension processes from the unpaved soil or the road dust) and, at least for the Mediterranean region, a contribution of long-range advection of Saharan dust (Cesari et al. 2012; Pietrodangelo et al. 2013; Contini et al. 2014a). These sources, although different, have

extensive similarities in the chemical profiles that may make the characterization of the crustal contribution very difficult. Further, marine aerosols can be a source of ambiguity in SA works as it can be present either in the form of Bfresh^ contribution and in the form of Baged^ contribution with a different role of nitrates (Contini et al. 2014b). Moreover, the phenomena of Cl depletion can strongly modify the ratio Cl/Na in relation to the characteristics and to the typical meteorological conditions of the site (Zhao and Gao 2008). Previous studies comparing the results of different RMs on the same datasets showed that the number and the estimated contributions of sources identified with different models may be different (Contini et al. 2012; Favez et al. 2010; Hopke et al. 2006; Larsen et al. 2008; Viana et al. 2008b; Stortini et al. 2009; Amato et al. 2009a; Tauler et al. 2009). This variability has been associated with the different theoretical approaches behind the models used. The need for standardization of RM application to source apportionment of atmospheric PM brought the European Commission’s Joint Research Centre (JRC) to organize an inter-comparison exercise for application of RMs to have a better understanding about the performances of different source apportionment methodologies and the comparability of their outputs (Karagulian et al. 2012). The present work aims to perform an inter-comparison of PM10 SA results obtained at three measurement sites: an Italian urban background site (Lecce), a Spanish urban background site (Barcelona), and a Spanish industrial site (Algeciras). The inter-comparison is performed using two receptor models (principal component analysis (PCA) and positive matrix factorization (PMF)) to investigate their performances in source identification (chemical profiles), in the quantification of source contributions, and in the stability and robustness of results as function of the number of chemical species included in the source apportionment.

Methods Description of the datasets used Three different datasets were used in this work. Two of them, obtained in Spain, were collected by the Department of Geosciences of the Institute of Environmental Assessment and Water Research (IDAEA)-Spanish Council for Scientific Research-CSIC (Barcelona, Spain). The third one, obtained in Italy, was collected by the Institute of Atmospheric Sciences and Climate, ISAC-CNR (Lecce, Italy). The first Spanish dataset was collected in Barcelona (in the following also indicated as BCN), from 2003 to 2007, at an urban background monitoring station, using MCV highvolume (30 m3/h) samplers equipped with DIGITEL PM10, PM2.5, and PM1 inlets. Particles were collected daily on quartz

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fiber filters and chemically analyzed following the procedures described by Querol et al. (2001) using elemental analyzer for total carbon (TC), inductively coupled plasma mass (ICPMS), and atomic emission spectrometry (ICP-AES) for determination of elemental concentrations, ion chromatography for NO3− and Cl−, and specific ion electrode for NH4+. In this work, the analysis was focused on the PM10 fraction, and the chemical species used in source apportionment were Al, Ca, K, Na, Mg, Fe, Mn, Ti, P, S, V, Cr, Ni, Cu, Zn, As, Rb, Sr, Cd, Sn, Sb, Pb, NH4+, NO3−, Cl−, and total carbon (TC). In total, the dataset has 243 samples and 26 chemical species with a characterization of 55 % of PM10 mass. Major details about this dataset are available in Amato et al. (2009a). The second Spanish dataset was collected in the Bay of Algeciras (in the following also indicated as AL), from 2003 to 2007, at four urbanized areas classified as urban background with industrial influence sites, using high-volume samplers equipped with PM10 and PM2.5 inlets (TISCH or Grasbey-Andersen, 68 m3/h). Particles were collected daily on quartz filters, and determination of major and trace elements was performed by a combination of analytical tools including ICP-MS, ICP-AES, ion chromatography, selective electrode, and elemental analysis, according to the methodology described by Querol et al. (2008). Also for this dataset, the analysis was focused on the PM10 fraction characterized by the following chemical species: Al, Ca, K, Na, Mg, Fe, Mn, Ti, P, V, Cr, Ni, Cu, Zn, As, Se, Rb, Sr, Sn, Sb, Pb, Li, La, Cl−, NH4+, NO3−, SO42−, and TC. In total, the dataset has 567 samples for 28 chemical species representing 60 % of PM10. Major details about this dataset are available in Pandolfi et al. (2011). The third dataset was collected in Lecce (in the following also indicated as LE), at an urban background site. PM10 has been simultaneously collected on Teflon and quartz filters. Soluble ionic species, SO42−, NO3−, NH4+, Cl−, Na+, K+, Mg2+, and Ca2+, have been analyzed via high-performance ion chromatography (HPIC), while elements Ni, Cu, V, Mn, As, Pb, Cr, and Sb have been analyzed via graphite furnace atomic absorption spectroscopy (GF-AAS) and elements Fe, Al, Zn, and Ti by ICP-AES. In total, the dataset has 91 daily samples, collected between January 2007 and January 2008, with 17 chemical species corresponding to a characterization of 40 % of PM10 mass. Further details about this dataset are available in Contini et al. (2010). The number of samples for the LE site is significantly lower with respect to AL and BCN sites; however, it is a sufficiently large number to obtain stable results in factor analysis according to typical thresholds indicated in different statistical criteria (Henry et al. 1984; Thurston and Spengler 1985). It is worth to specify that source apportionment results for the three datasets have been already published in other scientific works (Amato et al. 2009a; Contini et al. 2010; Pandolfi et al. 2011). However, the main difference between this work

and the previous publications is that this study is focused only on the PM10 fraction (as this is the only fraction commonly present for the three sites); thus, PM2.5 and PM1 fractions were excluded for BCN dataset and PM2.5 fraction for AL dataset. For each samples collected at the three sites, uncertainties on concentrations of chemical species were estimated taking in account errors coming from the analytical procedure, from the subtraction of blank filters for the different chemical species, and from the errors related to sampling procedure as described in Amato et al. (2009a) for the Barcelona dataset, Pandolfi et al. (2011) for the Algeciras dataset, and Contini et al. 2010 for the Lecce dataset. In Table 1, the average concentrations of PM10 and of the different chemical species, together with minimum and maximum concentration values, are reported. Datasets handling for sensitivity tests The number of chemical species is different in the three datasets, and, specifically, the LE has a significantly lower number of chemical species available with respect to BCN and AL datasets. Therefore, it has been decided to perform two types of analysis with receptor models. The first analysis using the complete datasets (i.e., the original ones) and the second on incomplete datasets in which the BCN and LA datasets have been reduced in order to have more comparable sets of chemical species in the three datasets. Specifically, in the BCN dataset, elements excluded were TC, Ti, P, Rb, Sr, As, Cd, Sn, and Sb; all these chemical species accounted for 20.8 % of PM10 mass (TC alone accounting for 20.6 %). In the AL dataset, elements excluded were TC, Li, P, Ti, As, Se, Rb, Sr, Sn, Sb, and La; all these chemical species accounted for 12.9 % of PM10 mass (TC accounting for 12.7 %). The intercomparison of SA performed on complete and incomplete datasets allowed to study how the presence or absence of specific chemical species could influence the SA results and if the models provide stable solutions in terms of both source profiles and source contributions. The PCA and PMF receptor models Source apportionment was performed using receptor models, which are based on the mass conservation principle: xi j ¼

p X

gik f k j þ ei j i ¼ 1; 2; ⋅⋅⋅; m j ¼ 1; 2; ⋅⋅⋅; n ð1Þ

k¼1

where xij is the jth species concentration measured in the ith sample, gik is the contribution of the kth source to ith sample, and fjk is the concentration of the jth species in kth source where eij is the residual for each sample/species. In the case in which both number/nature of aerosol sources fjk, and their contributions gik, are unknowns, factor analysis approach, such as the

Environ Sci Pollut Res Table 1 Average, minimum, and maximum concentrations of PM10 and of the different chemical species analyzed in the three sites

Barcelona (BCN, ng/m3)

Algeciras (AL, ng/m3)

Lecce (LE, ng/m3)

PM10 TC Al

42,646.7 (14,071.0–105,737.7)

37,326.8 (5609.0–115,870.1)

26,270.8 (6391.5–75,470.0)

8550.9 (2250.6–20,120.3) 652.9 (102.5–2810.0)

4313.5 (100.0–20,561.2) 506.5 (14.5–4114.2)

– 310.5 (20.9–3811.7)

Ca K

2499.9 (209.8–7898.5) 358.1 (87.9–1005.8)

1393.9 (79.2–6833.8) 269.9 (10.0–1115.1)

940.1 (100.6–5561.5) 282.1 (71.2–1002.5)

Na

991.4 (105.5–3925.7)

1933.2 (43.2–10,752.1)

807.4 (81.3–2758.1)

Mg

271.3 (50.5–761.9)

366.1 (45.5–1522.4)

192.6 (31.6–722.9)

Fe Mn

945.5 (212.6–3154.8) 18.8 (3.8–47.2)

441.8 (18.2–2911.7) 15.2 (0.6–126.8)

306.0 (12.0–2231.4) 8.0 (0.5–59.5)

Ti P

40.2 (6.1–169.0) 27.2 (3.8–113.0)

28.2 (0.5–228.8) 23.1 (0.01–198.8)

15.7 (0.1–90.1) –

S SO42−

1555.4 (249.4–4777.4) –

– 6238.7 (420.5–27,451.8)

– 3595.9 (525.8–7967.0)

V Cr

11.3 (1.1–45.2) 5.5 (0.7–20.6)

24.9 (0.7–286.0) 16.4 (0.05–281.6)

1.6 (0.1–6.3) 2.2 (0.2–41.7)

Ni Cu Zn As Rb

5.3 (0.3–28.1) 68.3 (13.6–435.0) 92.2 (3.0–608.2) 0.9 (0.2–4.6) 1.2 (0.1–3.9)

16.6 (0.1–157.9) 18.6 (0.1–132.4) 66.3 (0.01–1471.7) 0.7 (0.03–5.8) 0.7 (0.03–4.8)

3.0 (0.2–46.7) 12.9 (0.8–114.2) 24.8 (2.2–215.3)