“…It can be found at SPECIEUROPE ( https://source-apportionment.jrc.ec.europa.eu/Specieurope/index.aspx ). Through the leadership of the European Union's Joint Research Centre in Ispra, guidance on how to perform source apportionment studies ( Belis et al, 2014 , Belis et al, 2019 ; Thunis et al, 2020 ) and several intercomparison studies ( Hopke et al, 2006 ; Belis et al, 2015 , Belis et al, 2020 ) have been conducted to improve the skills in using these techniques across Europe.…”
Source apportionments have become increasingly performed to determine the origins of ambient particulate pollution. The results can be helpful in designing mitigation strategies to improve air quality. Source specific particulate matter (PM) concentrations are also being used in health effects studies to be able to focus attention on those sources most likely to be responsible for the observed adverse health effects. In 2015, the World Health Organization (WHO) released its initial compilation of source apportionment studies published through August 2014. This initial database was described by Karagulian et al. (Atmospheric Environment120 (2015) 475–483). In the present report, a new compilation has been prepared of those apportionments published since 2014 through December 2019. In addition, the database has been expanded to include apportionments of heavy metals, water-soluble components, and carbonaceous components in ambient PM. As a result of this work, we have developed and presented some perspectives on source apportionment going forward. We also have made a series of recommendations for source apportionment studies and reporting them. It is essential for papers to provide a minimum set of information so that the study can be adequately assessed, and the results utilized by others in making policy decisions or as part of other scientific studies.
“…It can be found at SPECIEUROPE ( https://source-apportionment.jrc.ec.europa.eu/Specieurope/index.aspx ). Through the leadership of the European Union's Joint Research Centre in Ispra, guidance on how to perform source apportionment studies ( Belis et al, 2014 , Belis et al, 2019 ; Thunis et al, 2020 ) and several intercomparison studies ( Hopke et al, 2006 ; Belis et al, 2015 , Belis et al, 2020 ) have been conducted to improve the skills in using these techniques across Europe.…”
Source apportionments have become increasingly performed to determine the origins of ambient particulate pollution. The results can be helpful in designing mitigation strategies to improve air quality. Source specific particulate matter (PM) concentrations are also being used in health effects studies to be able to focus attention on those sources most likely to be responsible for the observed adverse health effects. In 2015, the World Health Organization (WHO) released its initial compilation of source apportionment studies published through August 2014. This initial database was described by Karagulian et al. (Atmospheric Environment120 (2015) 475–483). In the present report, a new compilation has been prepared of those apportionments published since 2014 through December 2019. In addition, the database has been expanded to include apportionments of heavy metals, water-soluble components, and carbonaceous components in ambient PM. As a result of this work, we have developed and presented some perspectives on source apportionment going forward. We also have made a series of recommendations for source apportionment studies and reporting them. It is essential for papers to provide a minimum set of information so that the study can be adequately assessed, and the results utilized by others in making policy decisions or as part of other scientific studies.
“…The process of identifying PM sources is called source apportionment and there is a wide range of methods that have been proposed based on the available measurements [6]. A widely used source apportionment approach is the use of various types of source-receptor models [7][8][9][10][11]. Most receptor models are based on the assumption that at the receptor site, the time dependence of the concentration of PM components originating from the same source will be the same.…”
Fine particulate matter (PM) originates from various emission sources and physicochemical processes. Quantification of the sources of PM is an important step during the planning of efficient mitigation strategies and the investigation of the potential risks to human health. Usually, source apportionment studies focus either on the organic or on the inorganic fraction of PM. In this study that took place in Patras, Greece, we address both PM fractions by combining measurements from a range of on- and off-line techniques, including elemental composition, organic and elemental carbon (OC and EC) measurements, and high-resolution Aerosol Mass Spectrometry (AMS) from different techniques. Six fine PM2.5 sources were identified based on the off-line measurements: secondary sulfate (34%), biomass burning (15%), exhaust traffic emissions (13%), nonexhaust traffic emissions (12%), mineral dust (10%), and sea salt (5%). The analysis of the AMS spectra quantified five factors: two oxygenated organic aerosols (OOA) factors (an OOA and a marine-related OOA, 52% of the total organic aerosols (OA)), cooking OA (COA, 11%) and two biomass burning OA (BBOA-I and BBOA-II, 37% in total) factors. The results of the two methods were synthesized, showcasing the complementarity of the two methodologies for fine PM source identification. The synthesis suggests that the contribution of biomass burning is quite robust, but that the exhaust traffic emissions are not due to local sources and may also include secondary OA from other sources.
“…Such effect, however, affects the biomass burning only to a limited extent because its contribution to the formation of ammonium nitrate and ammonium sulphate is very limited; on average only 6% of the mass allocated to fresh (not aged) biomass burning corresponds to these compounds (SPECIEUROPE; Pernigotti et al, 2016). For sources contributing substantially to the secondary inorganic aerosol, the comparison between RMs and CTM brute force approach is not straightforward (Belis et al, 2019). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…More specifically, the SHERPA tool is defined through statistical relations: a set of cell-dependent coefficients known as source receptor relationships (SRR), trained and validated using data coming from a CTM run for a specific set of simulations. SHERPA implements a source apportionment approach known as brute force or emission reduction impact (Thunis et al, 2019; Belis et al, 2019). A more detailed description of the SHERPA methodology can be found in Pisoni et al (2017).…”
The SHERPA tool was used to assess the major pollution sources and the geographical areas impacting on the PM2.5 of the main cities in the Danube and Western Balkans regions. The activity sectors influencing most the PM2.5 levels in the study area are energy production (22%), agriculture (19%), residential combustion (16%) and road transport (7%). The energy production in inefficient coal-fuelled power plants was identified as one of main source of PM2.5 in the Western Balkans. As for the geographical origin of PM2.5, the transboundary pollution is confirmed as the main origin of PM2.5 (44%) in the investigated cities, while the city own emissions and the national sources outside the concerned city impact on average 22% and 15%, respectively. An association was observed between the long-range transport and the impact of agriculture and energy production, while both local urban emissions and long-range transport were associated with the residential sector. A special attention is given in this study to biomass, a renewable source, which use is often promoted in the frame of climate and energy policies. Nevertheless, the combustion of biomass in inefficient small appliances has considerable particulate matter emissions and therefore this type of practice impacts negatively on air quality. Considering that biomass is traditionally used in South-East Europe as fuel for residential heating, the interpretation of the model results was supported with the estimation of biomass burning contributions to PM2.5 obtained with receptor models and data on biomass fuel consumption from the literature. The analysis of the contributions from biomass burning derived from receptor models suggests that biomass burning is the dominant source within the residential heating sector in the studied area and that the emissions from this source are likely underestimated. This study concludes that more effort is needed to improve the estimations of biomass burning emissions and that policies to improve air quality in the cities should involve a geographic context wider than the city level.
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