The most common environmental metrics for PM are the annual or 24-hr mean mass concentration of fine particles, PM2.5, but other metrics are also used, including the mass concentration of particles with a diameter of less than 10 micrometres, PM10.. Based on scientific consensus, the WHO sets guideline values for PM, noting that even these levels may lead to health effects as there is likely no lower limit for many health outcomes (e.g., WHO, 2021[1]).
Global average estimates take into account PM mass, assuming that PM are the same the world around, which is a major simplification (Li et al, 2019)[2]. PM2.5 is a time- and spatially unique cocktail of chemicals (hydrocarbons, salts and other compounds given off by e.g., vehicles, cooking stoves and industry, and of natural components such as natural dust and microorganisms. This mixture and its toxicity vary in time and space, and levels of PM2.5 alone give only a rough indication of toxicity in a particular place (Brunet et al, 2018)[3]. Reducing PM2.5 by the same amount in different places will not deliver the same health benefits everywhere. To protect more lives, the role of researchers is to determine the most hazardous constituents of air pollution and mitigate them by priority.
Sources of mass concentrations of PM10 and PM2.5 have been identified and quantified in different cities and regions around the world (e.g. Amato et al., 2016[4]). Less is known about the sources and their contribution to smaller particles, e.g., UFP (particles ≤0.1 μm) characterized by particle number concentrations (PNC) (Vu et al., 2015). Recently published papers about source apportionment and mortality where it was included source apportionment in the study on 4 European cities[5], [6], These cities are characterised by different meteorological conditions and emissions. The common sources identified across all stations were Photonucleation, Traffic nucleation, Fresh traffic and Urban (influenced by other sources in some cities), and Secondary particles.
Carbonaceous aerosol often the largest component of fine particulate matter. Carbon aerosol particles, composed of light-scattering Organic Carbon, OC, and light-absorbing carbonaceous aerosols, dominated by Black Carbon, BC or Elemental Carbon (EC). The relation between BC and EC is close, the thermally refractive EC usually corresponds to the light-absorbing BC, the sooty black material emitted from gas and diesel engines, biomass burning, coal-fired power plants, and other sources that burn fossil fuel. Presence of BC has negative effects for both, human health and our climate (Novakov and Rosen, 2013, op.cit.4). Inhalation of BC is associated with health problems including respiratory and cardiovascular disease, cancer, and even birth defects (Janssen et al., 2011[7] and 2012[8]).
Several analytical methods that separate EC from organic OC and inorganic carbon (carbonate) employ temperature-programmed heating of the sample in an oxidizing or inert atmosphere and detecting the gases evolving from the sample upon heating. We will implement off-line and on-line determination of OC and EC. For off-line analyses from quartz filters we will use Thermal-optical analysis, Lab OC-EC Aerosol Analyzer Sunset Laboratory instrument, and follow the thermal protocol EUSAAR_2 (EN 16909, 2015), (Karanasiou et al., 2015) [9], [10] .
Several studies have used multi wavelength measurements of the absorption coefficient for source apportionment of equivalent black carbon (eBC) in ambient air, using the Aethalometer from Magee Scientific, by assuming a priori aerosol absorption Ångstrøm exponents (AAE) for fossil or liquid fuel combustion or solid fuel combustion (Sandradewi et al., 2008[11]; Zotter et al., 2017[12]). This instrument will be installed at Belgrade. We will then use a novel positive matrix factorization (PMF) application, finding two factors in absorption obtaining as results the source eBC fractions and the source-specific AAE values: one with a low AAE factor and one with a higher AAE factor, representing fossil or liquid fuel combustion or solid fuel combustion, respectively. The advantages of this PMF approach are that no a-priori knowledge of the factor AAEs is required (rather AAEs are output variables), no periods of negative concentration result, deviations from a strict power-law dependence of absorption on wavelength (e.g., due to degradation of light absorbing components in the atmosphere or instrument errors/bias) are permitted, and poorly fitting data are assigned to a residual.
Bootstrapping allows estimation of uncertainties. We will assess the relevance of the source profiles to human health via comparison to epidemiological data, yielding new site-specific metrics to assess the health risks of ambient aerosol in Belgrade.
It is important to link the chemical species and sources of ambient PM with oxidative potential, to provide critical information needed to effectively reduce emissions from sources that release PM with greater toxicity. Various source-apportionment methods have been used to better understand the processes contributing to and influencing the airborne concentrations and the residence time of PM in the atmosphere. They include direct modelling approaches such as chemistry transport models (CTMs) using tagged species (Brandt et al., 2013[13]; Kranenburg et al., 2013[14]; Mircea et al., 2020[15]; Wang et al., 2009) or field studies coupled with receptor models (RMs) (Belis et al., 2020[16]; Pernigotti et al., 2016[17]), and specifically PMF that can be based either on an aerosol mass spectrometer (AMS) time-resolved spectrum (Bozzetti et al., 2017[18]; Zhang et al., 2019[19]) or on filter analysis (Amato et al., 2016[20]; Liu et al., 2016[21]; Petit et al., 2019[22]; Salameh et al., 2018[23]; Srivastava t al., 2018[24]) or a mix of these different techniques (Costabile et al., 2017[25]; Vlachou et al., 2018, 2019[26].[27]).
Source apportionment by PMF requires the inclusion of source-specific tracers to achieve separation of the sources. Most sources contribute equally to PM10 and PM2.5 except for crustal/mineral dust resuspension and sea/road salt, which prevail in the coarse fraction. In order to abate exceedance of air quality limits, Secondary Inorganic Aerosol (SIA) and traffic are the most important source categories to target throughout the year together with biomass burning during the cold season (Belis et al, 2013)[28]. A total of 419 source apportionment records from studies conducted in cities of 51 countries were used to calculate regional averages of sources of ambient particulate matter (Karagulian et al, 2015, op.cit.6). Based on the available information, globally 25% of urban ambient air pollution from PM2.5 is contributed by traffic, 15% by industrial activities, 20% by domestic fuel burning, 22% from unspecified sources of human origin, and 18% from, natural dust and salt. The available source apportionment records exhibit, however, important heterogeneities in assessed source categories and incompleteness in certain countries/regions, such as in Western Balkans region, including Serbia.
We will quantify ambient aerosol sources in Belgrade and Bor, using the ME-2 positive matrix factorization generalized multilinear engine (Canonaco et al., 2013)[29]. Data collected will include Organic (OC) and elemental carbon (EC), levoglucosan, mannosan, galactosan, arabitol, mannitol, trehalose, glucose, 2-methylerythritol, 2-methylthreitol, V, Mn, Ti, Fe, Co, Ni, Cu, Zn, As, Cd, and Pb, SO42-, NO3- , NH4+, Ca2+, Mg2+, K+, Na+, and Cl−, all measured in the PM10 size fraction. A minimum of 90 aerosol filter samples will be collected, covering all four seasons to reflect seasonal variability in the source composition. We emphasize the absolute necessity of including the source specific organic tracers for a successful outcome of the source apportionment. We will also install an aethalometer for novel application of PMF to absorption data for source apportionment of equivalent black carbon (eBC) to a fossil/liquid fuel combustion source and a solid fuel combustion source.
The mechanisms of PM toxicity are complex and not completely understood. One view is that PM toxicity occurs through inducement of oxidative stress (Delfino et al., 2005[30], 2013[31]; Nel, 2005[32]), a state of biochemical imbalance in which the presence and formation of reactive oxygen species (ROS) in the human body overwhelms antioxidant defense mechanisms, eventually leading to various dverse health outcomes (Delfino et al., 2011[33]; Donaldson et al., 2001[34]; Li et al., 2003[35]). ROS can be either transported on inhaled particles to the air‑lung interface, or generated in vivo by interaction between deposited PM and physiological-chemical components (Lakey et al., 2016[36]). The ability of PM to generate ROS is defined as the oxidative potential (OP) of PM. OP integrates various biologically relevant properties of particles, including size, surface area, and chemical composition, which may better reflect the biological response to PM exposure and consequently be more informative than the particle mass concentration (PM). OP is also related to the specific PM chemical speciation[37].
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[27] Belis, C. A., Pernigotti, D., Pirovano, G. et al. (2020) Evaluation of Receptor and Chemical Transport Models for PM10 Source Apportionment, Atmos. Environ. X, 5, 100053, https://doi.org/10.1016/j.aeaoa.2019.100053, 2020.
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[27] Bozzetti, C., Sosedova, Y., Xiao, M. et al. (2017) Argon offline-AMS source apportionment of organic aerosol over yearly cycles for an urban, rural, and marine site, Atmos. Chem. Phys., 17, 117–141, https://doi.org/10.5194/acp-17-117-2017
[27] Zhang, Y., Favez, O., Petit, J.-E. et al. (2019) Six-year source apportionment of submicron organic aerosols ..Atmos. Chem. Phys., 19, 14755–14776, https://doi.org/10.5194/acp-19-14755-2019
[27]Amato, F., Alastuey, A., Karanasiou, A., et al. (2016) AIRUSE-LIFE+: a harmonized PM speciation and source apportionment in five southern European cities, Atmos. Chem. Phys., 16, 3289–3309, https://doi.org/10.5194/acp-16-3289-2016
[27] Liu, Q., Baumgartner, J., Zhang, Y., and Schauer, J. J. (2016) Source Apportionment of Beijing Air Pollution during a Severe Winter Haze Event, Atmos. Environ., 126, 28–35, https://doi.org/10.1016/j.atmosenv.2015.11.031
[27] Petit, J.-E., Pallarès, C., Favez, O. et al. (2019) Sources and Geographical Origins of PM10 in Metz (France) Using Oxalate as a Marker of Secondary Organic Aerosols by PMF, Atmosphere, 10, 370, https://doi.org/10.3390/atmos10070370
[27]Salameh, D., Pey, J., Bozzetti, C. et al. (2018) Sources of PM2.5 at an Urban-Industrial Mediterranean City, Marseille (France): Application of the ME-2 Solver, Atmos. Res., 214, 263–274, https://doi.org/10.1016/j.atmosres.2018.08.005
[27] Srivastava, D., Tomaz, S., Favez, O. et al. (2018) Speciation of organic fraction foes fatter for fource apportionment Part 1: A One-Year Campaign in Grenoble, Sci. Total Environ., 624, 1598–1611, https://doi.org/10.1016/j.scitotenv.2017.12.135
[27] Costabile, F., Alas, H., Aufderheide, M. (2017) First Results of the “Carbonaceous Aerosol in Rome and Environs (CARE)” Experiment: Beyond Current Standards for PM10, Atmosphere, 8, 249, https://doi.org/10.3390/atmos8120249
[27] Vlachou, A., Daellenbach, K. R., Bozzetti, C. et al. (2018) Advanced source apportionment of carbonaceous aerosols by coupling offline AMS and radiocarbon.., Atmos. Chem. Phys., 18, 6187–6206, https://doi.org/10.5194/acp-18-6187-2018
[27] Vlachou, A., Tobler, A., Lamkaddam, H. et al. (2019) Development of a versatile source apportionment analysis based on positive matrix factorization: a case study.., Atmos. Chem. Phys., 19, 7279–7295, https://doi.org/10.5194/acp-19-7279-2019
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