AIRUSE-LIFE+: a harmonized PM speciation and source apportionment in five southern European cities

Abstract

Abstract. The AIRUSE-LIFE+ project aims at characterizing similarities and heterogeneities in particulate matter (PM) sources and contributions in urban areas from southern Europe. Once the main PMx sources are identified, AIRUSE aims at developing and testing the efficiency of specific and non-specific measures to improve urban air quality. This article reports the results of the source apportionment of PM10 and PM2.5 conducted at three urban background sites (Barcelona, Florence and Milan, BCN-UB, FI-UB and MLN-UB), one suburban background site (Athens, ATH-SUB) and one traffic site (Porto, POR-TR). After collecting 1047 PM10 and 1116 PM2.5 24 h samples during 12 months (from January 2013 on) simultaneously at the five cities, these were analysed for the contents of OC, EC, anions, cations, major and trace elements and levoglucosan. The USEPA PMF5 receptor model was applied to these data sets in a harmonized way for each city. The sum of vehicle exhaust (VEX) and non-exhaust (NEX) contributes between 3.9 and 10.8 µg m−3 (16–32 %) to PM10 and 2.3 and 9.4 µg m−3 (15–36 %) to PM2.5, although a fraction of secondary nitrate is also traffic-related but could not be estimated. Important contributions arise from secondary particles (nitrate, sulfate and organics) in PM2.5 (37–82 %) but also in PM10 (40–71 %), mostly at background sites, revealing the importance of abating gaseous precursors in designing air quality plans. Biomass burning (BB) contributions vary widely, from 14–24 % of PM10 in POR-TR, MLN-UB and FI-UB, 7 % in ATH-SUB, to < 2 % in BCN-UB. In PM2.5, BB is the second most important source in MLN-UB (21 %) and in POR-TR (18 %), the third one in FI-UB (21 %) and ATH-SUB (11 %), but is again negligible (< 2 %) in BCN-UB. This large variability among cities is mostly due to the degree of penetration of biomass for residential heating. In Barcelona natural gas is very well supplied across the city and is used as fuel in 96 % of homes, while in other cities, PM levels increase on an annual basis by 1–9 µg m−3 due to biomass burning influence. Other significant sources are the following. – Local dust, 7–12 % of PM10 at SUB and UB sites and 19 % at the TR site, revealing a contribution from road dust resuspension. In PM2.5 percentages decrease to 2–7 % at SUB-UB sites and 15 % at the TR site. – Industry, mainly metallurgy, contributing 4–11 % of PM10 (5–12 % in PM2.5), but only at BCN-UB, POR-TR and MLN-UB. No clear impact of industrial emissions was found in FI-UB and ATH-SUB. – Natural contributions from sea salt (13 % of PM10 in POR-TR, but only 2–7 % in the other cities) and Saharan dust (14 % in ATH-SUB, but less than 4 % in the other cities). During high pollution days, the largest sources (i.e. excluding secondary aerosol factors) of PM10 and PM2.5 are VEX + NEX in BCN-UB (27–22 %) and POR-TR (31–33 %), BB in FI-UB (30–33 %) and MLN-UB (35–26 %) and Saharan dust in ATH-SUB (52–45 %). During those days, there are also quite important industrial contributions in BCN-UB (17–18 %) and local dust in POR-TR (28–20 %).

AIRUSE-LIFE +: estimation of natural source contributions to urban ambient air PM<sub>10</sub> and PM<sub>2. 5</sub> concentrations in southern Europe – implications to compliance with limit values

“…PMF analysis performed on the datasets of all five studied cities, reported in Amato et al (2016), has shown that a distinct African dust factor is not easily obtained. Only in the case of Florence was a separate PMF factor profile for African dust identified, providing a potential reference value for this city and insight into the chemical profile of transported African dust.…”

“…The measurement periods, monitoring sites and number of valid chemical speciation samples for each city are presented in Table 1 (Diapouli et al, 2017). Details about sites, sampling and analytical procedures are provided in detail in Amato et al (2016).…”

“…A biomass burning factor was obtained by receptor modelling (positive matrix factorisation, PMF), with several peak concentrations during the wildfires' period; thus, these concentrations were attributed to wildfires and classified as natural source contributions. Details on the PMF analysis and results are presented in Amato et al (2016).…”

“…The chemical profiles of mineral dust obtained by PMF (Amato et al, 2016) may provide further information on the discrepancies observed between the alternative methods. Comparison of the Athens mineral dust profile and the two mineral dust profiles obtained for Florence (for local and African dust), showed that the African dust profile differed with respect to the other two mineral dust profiles in the absence of organic carbon, Zn and Pb, while a much lower NO − 3 contribution was also observed.…”

Source Apportionment of Urban Particulate Matter using Hourly Resolved Trace Metals, Organics, and Inorganic Aerosol Components

“…(Tables 1 and 2) rather than being wind entrained soil particles. However, the midday peak in the diurnal pattern was not consistent with traffic alone and the crustal elements in its composition indicated that it was a mix of abrasion particles from vehicles and soil dust characterized by Al, Si, Ca, and Fe (e.g., Viana et al, 2008;Amato et al, 2016). A strong inverse correlation of the Road Dust factor with relative humidity and a higher contribution in drier months (e.g.…”