Source apportionment of PM10, organic carbon and elemental carbon at Swiss sites: an intercomparison of different approaches

Annual variation of source contributions to PM10 and oxidative potential in a mountainous area with traffic, biomass burning, cement-plant and biogenic influences

Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM10 sources and determined their contribution to oxidative potential (OP) as a health proxy for PM exposure in an Alpine valley influenced by cement industry. PM10 filter sample chemical analysis and equivalent black carbon (eBC) were measured at an urban background site from November 2020 to November 2021. Using an optimized Positive Matrix Factorization (PMF) model, the source chemical fingerprints and contributions to PM10 were determined. The OP assessed through two assays, ascorbic acid (AA) and dithiothreitol (DTT), was attributed to the PM sources from the PMF model with a multiple linear regression (MLR) model. Ten factors were found at the site, including biomass burning (34, 40 and 38% contribution to annual PM10, OPAA and OPDDT, respectively), traffic (14, 19 and 7%), nitrate- and sulphate-rich (together: 16, 5 and 8%), aged sea salt (2, 2 and 0%) and mineral dust (10, 12 and 17%). The introduction of innovative organic tracers allowed the quantification of the PM primary and secondary biogenic fractions (together: 13, 8 and 21%). In addition, two unusual factors due to local features, a chloride-rich factor and a second mineral dust-rich factor (named the cement dust factor) were found, contributing together 10, 14 and 8%. We associate these two factors to different processes in the cement plant. Despite their rather low contribution to PM10 mass, these sources have one of the highest OPs per µg of source. The results of the study provide vital information about the influence of particular sources on PM10 and OP in complex environments and are thus useful for PM control strategies and actions.

Identifying urban emission sources and their contribution to the oxidative potential of fine particulate matter (PM2.5) in Kuwait

In this study, we investigated the seasonal variations, chemical composition, sources, and oxidative potential of ambient PM2.5 (particles with a diameter of less than 2.5 μm) in Kuwait City. The sampling campaign was conducted within the premises of Kuwait Institute for Scientific Research from June 2022 to May 2023, covering different seasons throughout the year. The personal cascade impactor sampler (PCIS) operated at flow rate of 9 L/min was employed to collect weekly PM2.5 samples on PTFE and quarts filters. These collected samples were analyzed for carbonaceous species (i.e., elemental and organic carbon), metals and transition elements, inorganic ions, and DTT (dithiothreitol) redox activity. Furthermore, principal component analysis (PCA) and multi-linear regression (MLR) were used to identify the predominant emission sources and their percentage contribution to the redox activity of PM2.5 in Kuwait. The results of this study highlighted that the annual-averaged ambient PM2.5 mass concentrations in Kuwait (59.9 μg/m3) substantially exceeded the World Health Organization (WHO) guideline of 10 μg/m3. Additionally, the summer season displayed the highest PM2.5 mass concentration (75.2 μg/m3) compared to other seasons, primarily due to frequent dust events exacerbated by high-speed winds. The PCA identified four primary PM2.5 sources: mineral dust, fossil fuel combustion, road traffic, and secondary aerosols. The mineral dust was found to be the predominant source, contributing 36.1% to the PM2.5 mass, followed by fossil fuel combustion and traffic emissions with contributions of 23.7% and 20.3%, respectively. The findings of MLR revealed that road traffic was the most significant contributor to PM2.5 oxidative potential, accounting for 47% of the total DTT activity. In conclusion, this comprehensive investigation provides essential insights into the sources and health implications of PM2.5 in Kuwait, underscoring the critical need for effective air quality management strategies to mitigate the impacts of particulate pollution in the region.

Particulate matter from a tropical city in southeast Brazil: Impact of biomass burning on polycyclic aromatic compounds levels, health risks, and in vitro toxicity

In the context of a rising global temperature, biomass burning represents an increasing risk to human health, due to emissions of highly toxic substances such as polycyclic aromatic hydrocarbon (PAHs). Size-segregated particulate matter (PM) was collected in a region within the sugarcane belt of São Paulo state (Brazil), where biomass burning is still frequent, despite the phasing out of manual harvesting preceded by fire. The median of the total concentration of the 15 PAHs determined was 2.3 ± 1.8 ng m-3 (n = 19), where 63% of this content was in PM1.0. Concentrations of OPAHs and NPAHs were about an order of magnitude lower. PM2.5 collected in the dry season, when most of the fires occur, presented PAHs and OPAHs total concentrations three times higher than in the wet season, showing positive correlations with fire foci number and levoglucosan (a biomass burning marker). These results, added to the fact that biomass burning explained 65% of the data variance (PCA analysis), evidenced the importance of this practice as a source of PAHs and OPAHs to the regional atmosphere. Conversely, NPAHs appeared to be mainly derived from diesel-powered vehicles. The B[a]P equivalent concentration was estimated to be 4 times higher in the dry season than in the wet season, and was greatly increased during a local fire event. Cytotoxicity and genotoxicity of PM1.0 organic extracts were assessed using in vitro tests with human liver HepG2 cells. For both types of tests, significant toxicity was only observed for samples collected during the dry season. Persistent DNA damage that may have impaired the DNA repair system was also observed. The results indicated that there was a health risk associated with the air particulate mixture, mainly related to biomass burning, demonstrating the urgent need for better remediation actions to prevent the occurrence of burning events.

Seasonal Disparities of Human Health Risk and Particle-Bound Metal Characteristics Associated with Atmospheric Particles in a Fishery Harbor

The effects of atmospheric pollution from ship emissions have been considered for several harbors worldwide. The health risk assessment and source apportionment of particle-bound metals in a fishery harbor were investigated in this study. The most abundant metal elements in particulate matter (PM) on all sampling days in three seasons were Fe (280.94 ± 136.93 ng/m3), Al (116.40 ± 71.25 ng/m3), and Zn (110.55 ± 26.70 ng/m3). The ratios of V/Ni were 1.44 ± 0.31, 1.48 ± 0.09 and 1.87 ± 0.06 in PM10, PM2.5, and PM1, respectively. Meanwhile, the ratios higher than 1 indicated that fuel oil combustion from ship emission in fishery harbor. The highest deposits of total particle-bound metals in the human respiratory tract were in the head airway (HA), accounting for 76.77 ± 2.29% of the total particle-bound metal concentration, followed by 5.32 ± 0.13% and 2.53 ± 0.15% in the alveolar region (AR) and tracheobronchial (TB) region, respectively. The total cancer risk (CR) of inhalation exposure to local residents exceeded 10-6. Mean total CR values followed the sequence: autumn (1.24 × 10-4) > winter (8.53 × 10-5) > spring (2.77 × 10-6). Source apportionment of related metal emissions was mobile pollution emissions (vehicle/boat) (37.10-48.92%), metal fumes of arc welding exhaust (19.68-34.42%), spray-painting process (12.34-16.24%), combustion emissions (6.32-13.12%), and metal machining processes (9.04-16.31%) in Singda fishing harbor. These results suggest that proper control of heavy metals from each potential source in fishing harbor areas should be carried out to reduce the carcinogenic risk of adverse health effects.

Characterization of Aerosol Pollution in Two Hungarian Cities in Winter 2009–2010

In this study, atmospheric particulate matter (APM) pollution was compared in urban background sites of two cities in Hungary—namely the capital Budapest and Debrecen—by analyzing daily aerosol samples collected between 8 December 2009 and 18 March 2010. Concentration, elemental composition, including BC, and sources of fine (PM2.5) and coarse (PM2.5–10) aerosol pollution, as well as their variation due to meteorological conditions and anthropogenic activities, were determined for both cities. The average PM2.5 concentrations were 22 μg/m3 and 17 μg/m3 in Budapest and Debrecen, respectively. In the case of PM10, the mean concentration was 32 μg/m3 in Budapest and 23 μg/m3 in Debrecen. The concentration of the coarse fraction decreased significantly over the weekends compared to working days. The number of exceedances of the WHO recommended limit value for PM2.5 (15 μg/m3) were 67 in Budapest and 46 in Debrecen, which corresponds to 73% and 50% of the sampling days, respectively. At the time of the exceedances the daily average temperature was below freezing. The average PM2.5/PM10 ratio was 70% and 75% for the two sites, indicating the dominance of the fine fraction aerosol particles during the study period. Elements of natural origin (Al, Si, Ca, Ti, Mn, Fe, Ba) and chlorine were found to be dominant in the coarse fraction, while elements of anthropogenic origin (S, K, Cu, Zn, Pb) were characteristic to the fine fraction. Similar concentrations were measured in the two cities in the case of S which originates from regional transport and K which serves as a tracer for biomass combustion. Traffic-related elements were present in 2–3 times higher concentrations in Budapest. The episodic peaks in the Cl time series could be attributed to salting after snowfalls. The following sources of APM pollution were identified by using the EPA Positive Matrix Factorization (PMF) 5.0 receptor model: soil, traffic, road dust, secondary sulfate, biomass burning, and de-icing of streets. On polluted days when the PM2.5 concentration exceeded the 25 μg/m3 value the contribution of secondary sulfate, domestic heating, and traffic increased significantly compared to the average. On weekends and holidays the contribution of soil and traffic decreased. The main pollution sources and their contributions were similar to the ones in other cities in the region. Comparing our findings to results from winter 2015 it can be concluded that while the PM2.5 pollution level remained almost the same, a significant increase in the contribution of biomass burning was observed in both cities from 2010 to 2015, indicating a change of heating habits.

Impact of different sources on the oxidative potential of ambient particulate matter PM10 in Riyadh, Saudi Arabia: A focus on dust emissions

In this study, we employed Principal Component Analysis (PCA) and Multi-Linear Regression (MLR) to identify the most significant sources contributing to the toxicity of PM10 in the city center of Riyadh. PM10 samples were collected using a medium-volume air sampler during cool (December 2019-March 2020) and warm (May 2020-August 2020) seasons, including dust and non-dust events. The collected filters were analyzed for their chemical components (i.e., water-soluble ions, metals, and trace elements) as well as oxidative potential and elemental and organic carbon (EC/OC) contents. Our measurements revealed comparable extrinsic oxidative potential (P-value = 0.30) during the warm (1.2 ± 0.1 nmol/min-m3) and cool (1.1 ± 0.1 nmol/min-m3) periods. Moreover, we observed higher extrinsic oxidative potential of PM10 samples collected during dust events (~30% increase) compared to non-dust samples. Our PCA-MLR analysis identified soil and resuspended dust, secondary aerosol (SA), local industrial activities and petroleum refineries, and traffic emissions as the four sources contributing to the ambient PM10 oxidative potential in central Riyadh. Soil and resuspended dust were the major source contributing to the oxidative potential of ambient PM10, accounting for 31% of the total oxidative potential. Secondary aerosols (SA) were the next important source of PM10 toxicity in the area as they contributed to about 20% of the PM10 oxidative potential. Results of this study revealed the major role of soil and resuspended road dust on PM10 toxicity and can be helpful in adopting targeted air quality policies to reduce the population exposure to PM10.

Investigating the plausibility of a PMF source apportionment solution derived using a small dataset: A case study from a receptor in a rural site in Apulia - South East Italy

Results of a methodological study on the use of Positive Matrix Factorization (PMF) with smaller datasets are being reported in this work. This study is based on 29 PM₁₀ and 33 PM_2.5 samples from a receptor in a rural setup in Apulia (Southern Italy). Running PMF on the two size fractions separately resulted in the model not functioning correctly. We therefore, augmented the size of the dataset by aggregating the PM₁₀ and PM_2.5 data. The 5-factor solution obtained for the aggregated data was fairly rotationally stable, and was further refined by the rotational tools included in USEPA PMF version 5. These refinements include the imposition of constraints on the solution, based on our knowledge of the chemical composition of the aerosol sources affecting the receptor. Additionally, the uncertainties associated with this solution were fully characterised using the improved error estimation techniques in this version of PMF. Five factors in all, were isolated by PMF: ammonium sulfate, marine aerosol, mixed carbonaceous aerosol, crustal/Saharan dust and total traffic. The results obtained by PMF were further tested inter alia, by comparing them to those obtained by two other receptor modelling techniques: Constrained Weighted Non-negative Matrix Factorization (CW - NMF) and Chemical Mass Balance (CMB). The results of these tests suggest that the solution obtained by PMF, is valid, indicating that for this particular airshed PMF managed to extract most of the information about the aerosol sources affecting the receptor - even from a dataset with a limited number of samples.

Source apportionment of urban PM1 in Barcelona during SAPUSS using organic and inorganic components

Source apportionment of atmospheric PM1 is important for air quality control, especially in urban areas where high mass concentrations are often observed. Chemical analysis of molecular inorganic and organic tracer compounds and subsequently data analysis with receptor models give insight on the origin of the PM₁ sources. In the present study, four source apportionment approaches were compared with an extended database containing inorganic and organic compounds that were measured during an intensive sampling campaign at urban traffic and urban background sites in Barcelona. Source apportionment of the combined database, containing both inorganic and organic compounds, was compared with more conventional approaches using inorganic and organic databases separately. Traffic emission sources were identified in all models for the two sites. The combined inorganic and organic databases provided higher discrimination capacity of emission sources. It identified aerosols generated by regional recirculation of biomass burning, secondary biogenic organic aerosols, harbor emissions, and specific industrial emissions. In this respect, this approach identified a relevant industrial source situated at NE Barcelona in which a waste incinerator plant, a combined-cycle power plant, and an industrial glass complex are located. Models using both inorganic and organic molecular tracer compounds improve the source apportionment of urban PM.

Source Apportionment of Total Suspended Particles (TSP) by Positive Matrix Factorization (PMF) and Chemical Mass Balance (CMB) Modeling in Ahvaz, Iran

There is a compelling need for apportionment of pollutants' sources to facilitate their reduction through proper management plans. The present study was designed to determine the contribution of each possible source of total suspended particles in Ahvaz's ambient air using positive matrix factorization (PMF), chemical mass balance (CMB), and the SPECIATE database. The sampling program undertaken followed EPA's guidelines and finally resulted in 74 samples. The concentration of 33 elemental and 10 ionic species were measured during a whole year. Three modeling approaches were applied: PMF, the integrated use of PMF and CMB, and the integrated use of the SPECIATE database and CMB. Six sources were derived by PMF: crustal dust (30.6%), industrial and mining activities (25.4%), motor vehicles (23.4%), marine aerosols (11.5%), secondary inorganic aerosols (5.7%), and road dust (3.4%). The contributions of sources from PMF-CMB approach were crustal dust (32.9%), industrial and mining activities (20.9%), motor vehicles (19.7%), marine aerosols (11.1%), secondary inorganic aerosols (9.2%), and road dust (9.36%). Seven sources were derived by SPECIATE-CMB approach: crustal dust (23.2%), industrial and mining activities (20.1%), motor vehicles (17.5%), marine aerosols (12.4%), secondary inorganic aerosols (4.8%), road dust (5.3%), and "nondetermined sources" factor (16.7%). Despite the different contributions of sources, there is a noticeable consistency between the results of these approaches. Furthermore, because of the approved performance of combined receptor models in previous studies and the presence of sufficient data on the number of species and samples, the results of the PMF-CMB approach are possibly the most realistic among those of the three applied approaches.

Speciation of organic fraction does matter for source apportionment. Part 1: A one-year campaign in Grenoble (France)

PM₁₀ source apportionment was performed by positive matrix factorization (PMF) using specific primary and secondary organic molecular markers on samples collected over a one year period (2013) at an urban station in Grenoble (France). The results provided a 9-factor optimum solution, including sources rarely apportioned in the literature, such as two types of primary biogenic organic aerosols (fungal spores and plant debris), as well as specific biogenic and anthropogenic secondary organic aerosols (SOA). These sources were identified thanks to the use of key organic markers, namely, polyols, odd number higher alkanes, and several SOA markers related to the oxidation of isoprene, α-pinene, toluene and polycyclic aromatic hydrocarbons (PAHs). Primary and secondary biogenic contributions together accounted for at least 68% of the total organic carbon (OC) in the summer, while anthropogenic primary and secondary sources represented at least 71% of OC during wintertime. A very significant contribution of anthropogenic SOA was estimated in the winter during an intense PM pollution event (PM₁₀>50μgm^-3 for several days; 18% of PM₁₀ and 42% of OC). Specific meteorological conditions with a stagnation of pollutants over 10days and possibly Fenton-like chemistry and self-amplification cycle of SOA formation could explain such high anthropogenic SOA concentrations during this period. Finally, PMF outputs were also used to investigate the origins of humic-like substances (HuLiS), which represented 16% of OC on an annual average basis. The results indicated that HuLiS were mainly associated with biomass burning (22%), secondary inorganic (22%), mineral dust (15%) and biogenic SOA (14%) factors. This study is probably the first to state that HuLiS are significantly associated with mineral dust.

Source apportionment of PM10 and PM2.5 in major urban Greek agglomerations using a hybrid source-receptor modeling process

A hybrid source-receptor modeling process was assembled, to apportion and infer source locations of PM₁₀ and PM_2.5 in three heavily-impacted urban areas of Greece, during the warm period of 2011, and the cold period of 2012. The assembled process involved application of an advanced computational procedure, the so-called Robotic Chemical Mass Balance (RCMB) model. Source locations were inferred using two well-established probability functions: (a) the Conditional Probability Function (CPF), to correlate the output of RCMB with local wind directional data, and (b) the Potential Source Contribution Function (PSCF), to correlate the output of RCMB with 72h air-mass back-trajectories, arriving at the receptor sites, during sampling. Regarding CPF, a higher-level conditional probability function was defined as well, from the common locus of CPF sectors derived for neighboring receptor sites. With respect to PSCF, a non-parametric bootstrapping method was applied to discriminate the statistically significant values. RCMB modeling showed that resuspended dust is actually one of the main barriers for attaining the European Union (EU) limit values in Mediterranean urban agglomerations, where the drier climate favors build-up. The shift in the energy mix of Greece (caused by the economic recession) was also evidenced, since biomass burning was found to contribute more significantly to the sampling sites belonging to the coldest climatic zone, particularly during the cold period. The CPF analysis showed that short-range transport of anthropogenic emissions from urban traffic to urban background sites was very likely to have occurred, within all the examined urban agglomerations. The PSCF analysis confirmed that long-range transport of primary and/or secondary aerosols may indeed be possible, even from distances over 1000km away from study areas.

A review of AirQ Models and their applications for forecasting the air pollution health outcomes

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.

Sources of the PM10 aerosol in Flanders, Belgium, and re-assessment of the contribution from wood burning

From 30 June 2011 to 2 July 2012 PM10 aerosol samples were simultaneously taken every 4th day at four urban background sites in Flanders, Belgium. The sites were in Antwerpen, Gent, Brugge, and Oostende. The PM10 mass concentration was determined by weighing; organic and elemental carbon (OC and EC) were measured by thermal-optical analysis, the wood burning tracers levoglucosan, mannosan and galactosan were determined by gas chromatography/mass spectrometry, 8 water-soluble ions were measured by ion chromatography, and 15 elements were determined by a combination of inductively coupled plasma atomic emission spectrometry and mass spectrometry. The multi-species dataset was subjected to receptor modeling by PMF. The 10 retained factors (with their overall average percentage contributions to the experimental PM10 mass) were wood burning (9.5%), secondary nitrate (24%), secondary sulfate (12.6%), sea salt (10.0%), aged sea salt (19.2%), crustal matter (9.7%), non-ferrous metals (1.81%), traffic (10.3%), non-exhaust traffic (0.52%), and heavy oil burning (3.0%). The average contributions of wood smoke for the four sites were quite substantial in winter and ranged from 12.5 to 20% for the PM10 mass and from 47 to 64% for PM10 OC. Wood burning appeared to be also a notable source of As, Cd, and Pb. The contribution from wood burning to the PM10 mass and OC was also assessed by making use of levoglucosan as single marker compound and the conversion factors of Schmidl et al. (2008), as done in our previous study on wood burning in Flanders (Maenhaut et al., 2012). However, the apportionments were much lower than those deduced from PMF. It seems that the conversion factors of Schmidl et al. (2008) may not be applicable to wood burning in Flanders. From scatter plots of the PMF-derived wood smoke OC and PM versus levoglucosan, we arrived at conversion factors of 9.7 and 22.6, respectively.

Large chemical characterisation of PM10 emitted from graphite material production: Application in source apportionment

This work focuses on emissions from industrial sources that are still poorly understood in Europe, especially the "carbon industry". The study is based on two intensive sampling campaigns performed in a graphite material production plant for 2weeks in July 2013 and November 2013 in alpine valleys. The chemical characterization of PM10 was conducted at three sampling sites (outdoor and indoor sites) located inside one industrial area, which is considered as the highest emissions source of polycyclic aromatic hydrocarbons (PAHs) in the Arve valley. The identification of specific tracers among metals and trace elements is commonly used to characterize industrial emissions. In our study, original enrichment factors relative to the "rural exposed background" have been calculated, and the metallic fraction was not affected by this industrial source. In contrast, the organic fraction of PM10 has a number of features, providing a complete organic source profile and referred to as the "carbon industry". In particular, polycyclic aromatic sulfur heterocycles (PASH) have been largely detected from fugitive emissions with rather large concentrations. The average concentrations of benzo(b)naphtho(2,1-d)thiophene (BNT(2,1)) reached 2.35-6.56ng·m(-3) and 60.5-376ng·m(-3) for outdoor and indoor sites, respectively. The use of this reference profile in the chemical mass balance model (CMB) applied to samples collected in two sites near industrial areas shows that this source had an average contribution of 6% of the organic matter (OM) mass during the sampling period during the winter of 2013.

Spatial and temporal variability of source contributions to ambient PM10 during winter in Augsburg, Germany using organic and inorganic tracers

Daily PM10 samples were collected during a one-month sampling campaign from February 13 to March 12, 2008 at eight different sampling sites in Augsburg, Southern Germany. Source apportionment was performed to identify the main sources and related contributions by analysis of organic and inorganic tracers. Nine factors were separated comprising: solid fuel combustion, traffic-related emissions, secondary inorganics, and mixed sources. Spatiotemporal variation of the source contributions was evaluated using the Pearson correlation coefficient (r) and coefficient of divergence (COD). All factors (except hopanes and mixed sources) showed moderate to high (0.6<r>0.8) correlation coefficients between the eight sites and were distributed heterogeneously. Secondary sulfate and secondary nitrate factors were relatively more uniformly distributed (compared to other factors) with lower medians of COD value (0.47 and 0.56, respectively) and higher correlation values (r=0.97 and 0.85, respectively). The maximum daily average contribution for coal & wood combustion factor was observed at the LfU suburban site (4.0 μg m(-3)); wood combustion factor at the LSW residential site (5.1 μg m(-3)) ; diesel & fuel oil consumption factor at the Bifa suburban and BP urban sites (both 2.5 μg m(-3)); road dust & tram factor at the KP traffic site (16.2 μg m(-3)) and the BP urban site (6.6 μg m(-3)); hopanes factor at the BP urban and Bifa suburban sites (both 0.7 μg m(-3)); and de-icing NaCl factor at the KP traffic site (4.8 μg m(-3)). Secondary sulfate and secondary nitrate factors had approximately similar contributions (6.2 μg m(-3) and 4.3 μg m(-3), respectively) at all sites. Mixed sources factor had the highest daily average contribution to PM10 mass at the KP traffic site (7.0 μg m(-3)).