Pattern Recognition Based Software for Oil Spills Identification by Gas-Chromatography and IR Spectrophotometry

For environmental control purposes, floating oil spills in harbours, off shore areas and their sources must often be identified. Pattern recognition, applied to IR spectrophotometric data (600–2000 cm⁻¹ range), and to chromatographic data (n -alkanes) for the spill and various suspected sources such as oil and fuels from ships bunkers and harbour installations, can lead to definite conclusions; particularly after artificial weathering formula are used. The software application provides quick and accurate identification of the pollution source. The identification algorithm has a learning stage in which the user creates a minimal database. This database has a tree structure with classes (fuels, crude, etc.) and members representing samples from already known sources. A sample contains IR and chromatographic data and information of the originating source. A larger database means more knowledge, which conveys a better identification. When the origin of an unknown sample is searched for, the software looks for the best match through the database and displays the results in two lists; sorted by calculated similarity. One list displays the classes in which the unknown sample could be included and the other displays the possible sources. An extra check can be done by visual inspection of the overlapped graphics (unknown sample and each of the identified sources).     

A Strategy and Methodology for Defensibly Correlating Spilled Oil to Source Candidates

The source of crude oils and petroleum products released into navigable waterways and shipping lanes is not always known. Thus, the defensible identification of spilled crude oils and petroleum products and their correlation to suspected sources is a critical part of many oil spill assessments. Quantitative "fingerprinting" analysis, when evaluated using straightforward statistical and numerical analyses, provides a defensible means to differentiate among qualitatively similar oils and provides the best assessment of the source(s) for spilled oils. Polycyclic aromatic hydrocarbon (PAH) and petroleum biomarker concentration data are a particularly useful quantitative measure that can benefit most oil spill investigations. In this paper the strategy and methodology for correlation analysis that relies upon quantitative gas chromatography/mass spectrometry operated in the selected ion monitoring mode (GC/MS-SIM) is demonstrated in a case study involving 66 candidate sources for a heavy fuel oil spill of unknown origin. The strategy includes identification of 19 chemical indices (out of 45 evaluated) based upon PAH's and biomarkers that were (1) independent of weathering; and (2) precisely measured, both of which are determined by statistical analysis of the data. The 19 chemical indices meeting these criteria are subsequently analysed using principal component analysis (PCA), which helps to determine defensibly the "prime suspects" for the oil spill under investigation. The strategy and methodology described, which combines statistical and numerical analysis of quantitative chemical data, can be adapted and applied to other environmental forensic investigations with the objective of correlating any form of contamination to its suspected sources.     

A Strategy and Methodology for Defensibly Correlating Spilled Oil to Source Candidates

The source of crude oils and petroleum products released into navigable waterways and shipping lanes is not always known. Thus, the defensible identification of spilled crude oils and petroleum products and their correlation to suspected sources is a critical part of many oil spill assessments. Quantitative “fingerprinting” analysis, when evaluated using straightforward statistical and numerical analyses, provides a defensible means to differentiate among qualitatively similar oils and provides the best assessment of the source(s) for spilled oils. Polycyclic aromatic hydrocarbon (PAH) and petroleum biomarker concentration data are a particularly useful quantitative measure that can benefit most oil spill investigations. In this paper the strategy and methodology for correlation analysis that relies upon quantitative gas chromatography/mass spectrometry operated in the selected ion monitoring mode (GC/MS-SIM) is demonstrated in a case study involving 66 candidate sources for a heavy fuel oil spill of unknown origin. The strategy includes identification of 19 chemical indices (out of 45 evaluated) based upon PAH's and biomarkers that were (1) independent of weathering; and (2) precisely measured, both of which are determined by statistical analysis of the data. The 19 chemical indices meeting these criteria are subsequently analysed using principal component analysis (PCA), which helps to determine defensibly the “prime suspects” for the oil spill under investigation. The strategy and methodology described, which combines statistical and numerical analysis of quantitative chemical data, can be adapted and applied to other environmental forensic investigations with the objective of correlating any form of contamination to its suspected sources.     

Oxygenates in Exhaust from Simple Hydrocarbon Fuels

Photochemical air pollution is known to be caused largely by automotive emissions such as hydrocarbons and oxygenated hydrocarbon derivatives. Unlike the hydrocarbons, the contribution of the oxygenates has been virtually unexplored, mainly because of lack of appropriate analytical methods. The objective of this study was to identify and estimate the levels of oxygenated hydrocarbon derivatives in exhaust from simple hydrocarbon fuels. This information is expected to yield ultimately estimates of the relative levels of various classes of oxygenates in exhaust from full-boiling-range gasolines. Identification and measurement of oxygenates in exhaust from the simplified fuels were accomplished using gas chromatography in conjunction with time-of-flight mass spectrometry. The analytical procedure involved concentration of the exhaust organics, followed by a two-stage chromatographic separation of the resultant mixture of oxygenates and hydrocarbons. Identified oxygenates in exhaust from nine test fuels included saturated and unsaturated aldehydes, ketones, and alcohols, as well as ethers, esters, and nitroalkanes; analytical data on organic acids were inconclusive. Of the identified noncarbonyl oxygenates, phenols, cyclic ethers and nitromethane appear to be relatively the most abundant.     

Oil Identification Based on a Goodness-of-Fit Metric Applied to Hydrocarbon Analysis Results

Assessment of environmental damage following accidental oil spills requires reliable oil identification methods. Results from hydrocarbon analyses of environmental samples are often difficult to interpret, because of the changes in oil composition (or weathering) that follows release into the environment, and because of confounding by hydrocarbons from other sources. To a first-order approximation, weathering proceeds according to simple first-order loss-rate (FOLR) kinetics for polycyclic aromatic hydrocarbons (PAH) based on molecular size. This relationship between relative weathering rate and molecular size can be exploited to infer the initial PAH composition of spilled oils, and this information can be combined with results for weathering-invariant analytes to substantially increase the precision and accuracy of hydrocarbon source recognition methods. The approach presented here evaluates a goodness-of-fit metric between the measured hydrocarbon composition of an environmental sample and a suspected source, after correcting for PAR weathering losses based on FOLR kinetics. Variability from analytical and sampling error may thus be accounted for, and source identifications can be expressed as objective probability statements. This approach is illustrated by application to four independent case studies.     

Oil Identification Based on a Goodness-of-Fit Metric Applied to Hydrocarbon Analysis Results

Assessment of environmental damage following accidental oil spills requires reliable oil identification methods. Results from hydrocarbon analyses of environmental samples are often difficult to interpret, because of the changes in oil composition (or weathering) that follows release into the environment, and because of confounding by hydrocarbons from other sources. To a first-order approximation, weathering proceeds according to simple first-order loss-rate (FOLR) kinetics for polycyclic aromatic hydrocarbons (PAH) based on molecular size. This relationship between relative weathering rate and molecular size can be exploited to infer the initial PAH composition of spilled oils, and this information can be combined with results for weathering-invariant analytes to substantially increase the precision and accuracy of hydrocarbon source recognition methods. The approach presented here evaluates a goodness-of-fit metric between the measured hydrocarbon composition of an environmental sample and a suspected source, after correcting for PAH weathering losses based on FOLR kinetics. Variability from analytical and sampling error may thus be accounted for, and source identifications can be expressed as objective probability statements. This approach is illustrated by application to four independent case studies.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

GIS-based source identification and apportionment of diffuse water pollution: perfluorinated compound pollution in the Tokyo Bay basin

To efficiently reduce perfluorinated compound (PFC) pollution, it is important to have an understanding of PFC sources and their contribution to the pollution. In this study, source identification of diffuse water pollution by PFCs was conducted using a GIS-based approach. Major components of the source identification were collection of the monitoring data and preparation of the corresponding geographic information that was extracted from a constructed GIS database. The spatially distributed pollution factors were then explored by multiple linear regression analysis, after which they were visually expressed using GIS. Among the 35 PFC homologues measured in a survey of the Tokyo Bay basin, 18 homologues were analyzed. Pollution by perfluorooctane sulfonate (PFOS) was explained well by the percentage of arterial traffic area in the basin, and the 84% variance of the measured PFOS concentration was explained by two geographic variables, arterial traffic area and population. Source apportionment between point and nonpoint sources was conducted based on the results of the analysis. The contribution of PFOS from nonpoint sources was comparable to that from point sources in several major rivers flowing into Tokyo Bay. Source identification and apportionment using the GIS-based approach was shown to be effective, especially for ubiquitous types of pollution, such as PFC pollution.     

Apportionment of TEQs from four major dioxin sources in Japan on the basis of five indicative congeners

The major sources of dioxins (polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and dioxin-like polychlorinated biphenyls (DL-PCBs)) in the environment in Japan have been considered to be combustion by-products, pentachlorophenol (PCP) formulations, chlornitrofen (CNP, 4-nitrophenyl-2,4,6-trichlorophenyl ether) formulations, and PCB products. Data on PCDDs, PCDFs and DL-PCBs from the four sources were analyzed, and indicative congeners whose concentrations were highly correlated with WHO-2006 toxic equivalencies (TEQs) were identified for each source sample. The indicative congeners for combustion by-products, PCP formulations, and CNP formulations were 2,3,4,7,8-pentachlorodibenzofuran, 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin, and 1,2,3,7,8-pentachlorodibenzo-p-dioxin, respectively; for PCB products, the indicative congeners were IUPAC Nos. #126- and #105-pentachlorobiphenyls. Moreover, using the data on PCDDs, PCDFs and DL-PCBs, we developed a set of equations for estimating the apportionment of TEQs from the four sources by using only the concentrations of the above-mentioned five indicative congeners. The equations were used along with the analysis results of different types of environmental samples collected from Japan, to determine the TEQ contributions of the four sources. The obtained values of TEQ contributions seemed to be reasonable. The estimation method was developed by using the data on major dioxin sources in Japan, and therefore, it is generally adaptable to environmental samples from any part of Japan. The method may be usable for regions outside Japan if source identification is carried out and the estimation equations are modified appropriately.     

Using Multiple Criteria for Fingerprinting Unknown Oil Samples Having Very Similar Chemical Composition

This paper describes a case study in which a multi-criterion approach was used to fingerprinting and identifying mystery oil samples. Three unknown oil samples were received from Quebec on March 28, 2001 for chemical analysis. The main purpose of this analysis was to detemine the nature and the type of the products, detailed hydrocarbon composition of the samples, and whether these samples came from the same source. The samples were analyzed by gas chromatography with a flame ionization detector (GC-FID) and by gas chromatography coupled with mass spectrometry (GC-MS). Hydrocarbon distribution patterns of unknown oils were recognized. Multiple suites of analytes were quantified and compared. A variety of diagnostic ratios of "source-specific marker" compounds for interpreting chemical data were further determined and analyzed. The chemical fingerprinting results reveal the following: (1) These three oils are most likely a hydraulic-fluid type oil. (2) These three oils are very "pure", largely composed of saturated hydrocarbons with the total aromatics being only 4-10% of the TPH. (3) The oils are a mixture of two different hydraulic fluids. There is no clear sign indicating they had been weathered. (4) The PAH concentrations are extremely low (<10 µg/g oil) in the oil samples, while the biomarker concentration are unusually high (4700-5500 µ/g oil). (5) Three major unknown compounds in the oil samples were positively identified. They are antioxidant compounds added to oils. (6) Samples 2996 and 2997 are identical and come from the same source. (7) The sample 2998 has group hydrocarbon compositions (including the GC traces, TPH, and total saturates) very similar to samples 2996 and 2997. But, it is not identical in chemical composition to samples 2996 and 2997, and they do not come from the same source.     

Using Multiple Criteria for Fingerprinting Unknown Oil Samples Having Very Similar Chemical Composition

This paper describes a case study in which a multi-criterion approach was used to fingerprinting and identifying mystery oil samples. Three unknown oil samples were received from Quebec on March 28, 2001 for chemical analysis. The main purpose of this analysis was to determine the nature and the type of the products, detailed hydrocarbon composition of the samples, and whether these samples came from the same source. The samples were analyzed by gas chromatography with a flame ionization detector (GC-FID) and by gas chromatography coupled with mass spectrometry (GC-MS). Hydrocarbon distribution patterns of unknown oils were recognized. Multiple suites of analytes were quantified and compared. A variety of diagnostic ratios of “source-specific marker” compounds for interpreting chemical data were further determined and analyzed. The chemical fingerprinting results reveal the following: (1) These three oils are most likely a hydraulic-fluid type oil. (2) These three oils are very “pure”, largely composed of saturated hydrocarbons with the total aromatics being only 4–10% of the TPH. (3) The oils are a mixture of two different hydraulic fluids. There is no clear sign indicating they had been weathered. (4) The PAH concentrations are extremely low (<10 μg/g oil) in the oil samples, while the biomarker concentration are unusually high (4700–5500 μg/g oil). (5) Three major unknown compounds in the oil samples were positively identified. They are antioxidant compounds added to oils. (6) Samples 2996 and 2997 are identical and come from the same source. (7) The sample 2998 has group hydrocarbon compositions (including the GC traces, TPH, and total saturates) very similar to samples 2996 and 2997. But, it is not identical in chemical composition to samples 2996 and 2997, and they do not come from the same source.     

Source profiles of particulate matter emissions from a pilot-scale boiler burning North American coal blends.

Recent awareness of suspected adverse health effects from ambient particulate matter (PM) emission has prompted publication of new standards for fine PM with aerodynamic diameter less than 2.5 microm (PM2.5). However, scientific data on fine PM emissions from various point sources and their characteristics are very limited. Source apportionment methods are applied to identify contributions of individual regional sources to tropospheric particulate concentrations. The existing industrial database developed using traditional source measurement techniques provides total emission rates only, with no details on chemical nature or size characteristics of particulates. This database is inadequate, in current form, to address source-receptor relationships. A source dilution system was developed for sampling and characterization of total PM, PM2.5, and PM10 (i.e., PM with aerodynamic diameter less than 10 pm) from residual oil and coal combustion. This new system has automatic control capabilities for key parameters, such as relative humidity (RH), temperature, and sample dilution. During optimization of the prototype equipment, three North American coal blends were burned using a 0.7-megawatt thermal (MWt) pulverized coal-fired, pilot-scale boiler. Characteristic emission profiles, including PM2.5 and total PM soluble acids, and elemental and carbon concentrations for three coal blends are presented. Preliminary results indicate that volatile trace elements such as Pb, Zn, Ti, and Se are preferentially enriched in PM2.5. PM2.5 is also more concentrated in soluble sulfates relative to total PM. Coal fly ash collected at the outlet of the electrostatic precipitator (ESP) contains about 85-90% PM10 and 30-50% PM2.5. Particles contain the highest elemental concentrations of Si and Al while Ca, Fe, Na, Ba, and K also exist as major elements. Approximately 4-12% of the materials exists as soluble sulfates in fly ash generated by coal blends containing 0.2-0.8% sulfur by mass. Source profile data for an eastern U.S. coal show good agreement with those reported from a similar study done in the United States. Based on the inadequacies identified in the initial sampling equipment, a new, plume-simulating fine PM measurement system with modular components for field use is being developed for determining coal combustion PM source profiles from utility boiler stacks.     

Source Profiles of Particulate Matter Emissions from a Pilot-Scale Boiler Burning North American Coal Blends

ABSTRACT

Recent awareness of suspected adverse health effects from ambient particulate matter (PM) emission has prompted publication of new standards for fine PM with aerodynamic diameter less than 2.5 μm (PM_2.5). However, scientific data on fine PM emissions from various point sources and their characteristics are very limited. Source apportionment methods are applied to identify contributions of individual regional sources to tropospheric particulate concentrations. The existing industrial database developed using traditional source measurement techniques provides total emission rates only, with no details on chemical nature or size characteristics of particulates. This database is inadequate, in current form, to address source-receptor relationships.

A source dilution system was developed for sampling and characterization of total PM, PM_2.5, and PM₁₀ (i.e., PM with aerodynamic diameter less than 10 μm) from residual oil and coal combustion. This new system has automatic control capabilities for key parameters, such as relative humidity (RH), temperature, and sample dilution. During optimization of the prototype equipment, three North American coal blends were burned using a 0.7-megawatt thermal (MW_t) pulverized coal-fired, pilot-scale boiler. Characteristic emission profiles, including PM_2.5 and total PM soluble acids, and elemental and carbon concentrations for three coal blends are presented.     

Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)

Contamination of groundwater constrains its uses and poses a serious threat to the environment. Once groundwater is contaminated, the cleanup may be difficult and expensive. Identification of unknown pollution sources is the first step toward adopting any remediation strategy. The proposed methodology exploits the capability of a universal function approximation by a feed-forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes, and duration of activity. The back-propagation algorithm is utilized for training the ANN to identify the source characteristics based on simulated concentration data at specified observation locations in the aquifer. Uniform random generation and the Latin hypercube sampling method of random generation are used to generate temporal varying source fluxes. These source fluxes are used in groundwater flow and the transport simulation model to generate necessary data for the ANN model-building processes. Breakthrough curves obtained for the specified pollution scenario are characterized by different methods. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentation is also performed with different number of training and testing patterns. In addition, the effects of measurement errors in concentration measurements values are used to show the robustness of ANN based methodology for source identification in case of erroneous data.     

Isotopic and molecular fractionation in combustion; three routes to molecular marker validation,including direct molecular ‘dating’ (GC/AMS)

The identification of unique isotopic, elemental, and molecular markers for sources of combustion aerosol has growing practical importance because of the potential effects of fine particle aerosol on health, visibility and global climate. It is urgent, therefore, that substantial efforts be directed toward the validation of assumptions involving the use of such tracers for source apportionment. We describe here three independent routes toward carbonaceous aerosol molecular marker identification and validation: (1) tracer regression and multivariate statistical techniques applied to field measurements of mixed source, carbonaceous aerosols; (2) a new development in aerosol ¹⁴C metrology: direct, pure compound accelerator mass spectrometry (AMS) by off-line GC/AMS (‘molecular dating’); and (3) direct observation of isotopic and molecular source emissions during controlled laboratory combustion of specific fuels. Findings from the combined studies include: independent support for benzo(ghi)perylene as a motor vehicle tracer from the first (statistical) and second (direct ‘dating’) studies; a new indication, from the third (controlled combustion) study, of a relation between ¹³C isotopic fractionation and PAH molecular fractionation, also linked with fuel and stage of combustion; and quantitative data showing the influence of both fuel type and combustion conditions on the yields of such species as elemental carbon and PAH, reinforcing the importance of exercising caution when applying presumed conservative elemental or organic tracers to fossil or biomass burning field data as in the first study.     

Source diagnostics of polycyclic aromatic hydrocarbons in urban road runoff, dust, rain and canopy throughfall

Diagnostic ratios and multivariate analysis were utilized to apportion polycyclic aromatic hydrocarbon (PAH) sources for road runoff, road dust, rain and canopy throughfall based on samples collected in an urban area of Beijing, China. Three sampling sites representing vehicle lane, bicycle lane and branch road were selected. For road runoff and road dust, vehicular emission and coal combustion were identified as major sources, and the source contributions varied among the sampling sites. For rain, three principal components were apportioned representing coal/oil combustion (54%), vehicular emission (34%) and coking (12%). For canopy throughfall, vehicular emission (56%), coal combustion (30%) and oil combustion (14%) were identified as major sources. Overall, the PAH's source for road runoff mainly reflected that for road dust. Despite site-specific sources, the findings at the study area provided a general picture of PAHs sources for the road runoff system in urban area of Beijing.     

Fossil fuels in the 21st century

An overview of the importance of fossil fuels in supplying the energy requirements of the 21st century, their future supply, and the impact of their use on global climate is presented. Current and potential alternative energy sources are considered. It is concluded that even with substantial increases in energy derived from other sources, fossil fuels will remain a major energy source for much of the 21st century and the sequestration of CO2 will be an increasingly important requirement.     

Risk assessment and prioritisation of contaminated sites on the catchment scale

Contaminated sites pose a significant threat to groundwater resources worldwide. Due to limited available resources a risk-based prioritisation of the remediation efforts is essential. Existing risk assessment tools are unsuitable for this purpose, because they consider each contaminated site separately and on a local scale, which makes it difficult to compare the impact from different sites. Hence a modelling tool for risk assessment of contaminated sites on the catchment scale has been developed. The CatchRisk screening tool evaluates the risk associated with each site in terms of its ability to contaminate abstracted groundwater in the catchment. The tool considers both the local scale and the catchment scale. At the local scale, a flexible, site specific leaching model that can be adjusted to the actual data availability is used to estimate the mass flux over time from identified sites. At the catchment scale, a transport model that utilises the source flux and a groundwater model covering the catchment is used to estimate the transient impact on the supply well. The CatchRisk model was tested on a groundwater catchment for a waterworks north of Copenhagen, Denmark. Even though data scarcity limited the application of the model, the sites that most likely caused the observed contamination at the waterworks were identified. The method was found to be valuable as a basis for prioritising point sources according to their impact on groundwater quality. The tool can also be used as a framework for testing hypotheses on the origin of contamination in the catchment and for identification of unknown contaminant sources.     

Assessment of source apportionment by Positive Matrix Factorization analysis on fine and coarse urban aerosol size fractions

This study was conducted in order to investigate the differences observed in source profiles in the urban environment, when chemical composition parameters from different aerosol size fractions are subjected to factor analysis. Source apportionment was performed in an urban area where representative types of emission sources are present. PM₁₀ and PM₂ samples were collected within the Athens Metropolitan area and analysed for trace elements, inorganic ions and black carbon. Analysis by two-way and three-way Positive Matrix Factorization was performed, in order to resolve sources from data obtained for the fine and coarse aerosol fractions. A difference was observed: seven factors describe the best solution in PMF3 while six factors in PMF2. Six factors derived from PMF3 analysis correspond to those described by the PMF2 solution for the fine and coarse particles separately. These sources were attributed to road dust, marine aerosol, soil, motor vehicles, biomass burning, and oil combustion. The additional source resolved by PMF3 was attributed to a different type of road dust. Combustion sources (oil combustion and biomass burning) were correctly attributed by PMF3 solely to the fine fraction and the soil source to the coarse fraction. However, a motor vehicle's contribution to the coarse fraction was found only by three-way PMF. When PMF2 was employed in PM₁₀ concentrations the optimum solution included six factors. Four source profiles corresponded to the previously identified as vehicles, road dust, biomass burning and marine aerosol, while two could not be clearly identified. Source apportionment by PMF2 analysis based solely on PM₁₀ aerosol composition data, yielded unclear results, compared to results from PMF2 and PMF3 analyses on fine and coarse aerosol composition data.