Regional source identification using Lagrangian stochastic particle dispersion and HYSPLIT backward-trajectory models

The main objective of this study was to investigate the capabilities of the receptor-oriented inverse mode Lagrangian Stochastic Particle Dispersion Model (LSPDM) with the 12-km resolution Mesoscale Model 5 (MM5) wind field input for the assessment of source identification from seven regions impacting two receptors located in the eastern United States. The LSPDM analysis was compared with a standard version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) single-particle backward-trajectory analysis using inputs from MM5 and the Eta Data Assimilation System (EDAS) with horizontal grid resolutions of 12 and 80 km, respectively. The analysis included four 7-day summertime events in 2002; residence times in the modeling domain were computed from the inverse LSPDM runs and HYPSLIT-simulated backward trajectories started from receptor-source heights of 100, 500, 1000, 1500, and 3000 m. Statistics were derived using normalized values of LSPDM- and HYSPLIT-predicted residence times versus Community Multiscale Air Quality model-predicted sulfate concentrations used as baseline information. From 40 cases considered, the LSPDM identified first- and second-ranked emission region influences in 37 cases, whereas HYSPLIT-MM5 (HYSPLIT-EDAS) identified the sources in 21 (16) cases. The LSPDM produced a higher overall correlation coefficient (0.89) compared with HYSPLIT (0.55-0.62). The improvement of using the LSPDM is also seen in the overall normalized root mean square error values of 0.17 for LSPDM compared with 0.30-0.32 for HYSPLIT. The HYSPLIT backward trajectories generally tend to underestimate near-receptor sources because of a lack of stochastic dispersion of the backward trajectories and to overestimate distant sources because of a lack of treatment of dispersion. Additionally, the HYSPLIT backward trajectories showed a lack of consistency in the results obtained from different single vertical levels for starting the backward trajectories. To alleviate problems due to selection of a backward-trajectory starting level within a large complex set of 3-dimensional winds, turbulence, and dispersion, results were averaged from all heights, which yielded uniform improvement against all individual cases.     

The impact of trajectory starting heights on the MURA trajectory source apportionment (TSA) method

Trajectory source apportionment (TSA) methods have been used in many research projects to attempt to identify the sources of pollution. Hybrid Single Particle Lagrangian Integrated Trajectories (HYSPLIT) is a popular model for use in various TSA methods. One of the options in this model is to choose a starting height. Very little research is available to assist a user in making this choice. This paper evaluates starting heights of 10, 50, 100, 250, and 500 m on the accuracy of the Multi-Receptor (MURA) method using artificial sources for three different simulations. It was found that using ensembles of trajectories in the MURA method appear to average out most of the biases found from different trajectory starting heights up to the 500 m tested.     

A hybrid modeling approach to resolve pollutant concentrations in an urban area

A modeling tool that can resolve contributions from individual sources to the urban environment is critical for air-toxics exposure assessments. Air toxics are often chemically reactive and may have background concentrations originated from distant sources. Grid models are the best-suited tools to handle the regional features of these chemicals. However, these models are not designed to resolve pollutant concentrations on local scales. Moreover, for many species of interest, having reaction time scales that are longer than the travel time across an urban area, chemical reactions can be ignored in describing local dispersion from strong individual sources making Lagrangian and plume-dispersion models practical. In this study, we test the feasibility of developing an urban hybrid simulation system. In this combination, the Community Multi-scale Air Quality model (CMAQ) provides the regional background concentrations and urban-scale photochemistry, and local models such as Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) and AMS/EPA Regulatory Model (AERMOD) provide the more spatially resolved concentrations due to local emission sources. In the initial application, the HYSPLIT, AERMOD, and CMAQ models are used in combination to calculate high-resolution benzene concentrations in the Houston area. The study period is from 18 August to 4 September of 2000. The Mesoscale Model 5 (MM5) is used to create meteorological fields with a horizontal resolution of 1×1 km². In another variation to this approach, multiple HYSPLIT simulations are used to create a concentration ensemble to estimate the contribution to the concentration variability from point sources. HYSPLIT simulations are used to model two sources of concentration variability; one due to variability created by different particle trajectory pathways in the turbulent atmosphere and the other due to different flow regimes that might be introduced when using gridded data to represent meteorological data fields. The ensemble mean concentrations determined by HYSPLIT plus the concentrations estimated by AERMOD are added to the CMAQ calculated background to estimate the total mean benzene concentration. These estimated hourly mean concentrations are also compared with available field measurements.     

Assimilation of conventional and satellite wind observations in a mesoscale atmospheric model for studying atmospheric dispersion

A mesoscale atmospheric model PSU/NCAR MM5 is used to provide operational weather forecasts for a nuclear emergency response decision support system on the southeast coast of India. In this study the performance of the MM5 model with assimilation of conventional surface and upper-air observations along with satellite derived 2-d surface wind data from QuickSCAT sources is examined. Two numerical experiments with MM5 are conducted: one with static initialization using NCEP FNL data and second with dynamic initialization by assimilation of observations using four dimensional data assimilation (FDDA) analysis nudging for a pre-forecast period of 12 h. Dispersion simulations are conducted for a hypothetical source at Kalpakkam location with the HYSPLIT Lagrangian particle model using simulated wind field from the above experiments. The present paper brings out the differences in the atmospheric model predictions and the differences in dispersion model results from control and assimilation runs. An improvement is noted in the atmospheric fields from the assimilation experiment which has led to significant alteration in the trajectory positions, plume orientation and its distribution pattern. Sensitivity tests using different PBL and surface parameterizations indicated the simple first order closure schemes (Blackadar, MRF) coupled with the simple soil model have given better results for various atmospheric fields. The study illustrates the impact of the assimilation of the scatterometer wind and automated weather stations (AWS) observations on the meteorological model predictions and the dispersion results.     

Estimating Gas Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model

Abstract

Manure storage tanks and animals in barns are important agricultural sources of methane. To examine the possibility of using an inverse dispersion technique based on a backward Lagrangian Stochastic (bLS) model to quantify methane (CH₄) emissions from multiple on-farm sources, a series of tests were carried out with four possible source configurations and three controlled area sources. The simulated configurations were: (C1) three spatially separate ground-level sources, (C2) three spatially separate sources with wind-flow disturbance, (C3) three adjacent ground-level sources to simulate a group of adjacent sources with different emission rates, and (C4) a configuration with a ground level and two elevated sources. For multiple ground-level sources without flow obstructions (C1 and C3), we can use the condition number (k, the ratio of the uncertainty in the calculated emission rate to the uncertainty in the predicted ratio of concentration to emission rate) to evaluate the applicability of this inverse dispersion technique and a preliminary threshold of k < 10 is recommended. For multiple sources with wind disturbance (C2) or an even more complex configuration including ground level and elevated sources (C4), a low k is not sufficient to provide reasonable discrete and total emission rates. The effect of flow obstructions can be neglected as long as the distance between the source and the measurement location is greater than approximately 10 times the height of the flow obstructions. This study shows that the bLS model has the potential to provide accurate discrete emission rates from multiple on-farm emissions of gases provided that certain conditions are met.     

Source estimation methods for atmospheric dispersion

Both forward and backward transport modeling methods are being developed for characterization of sources in atmospheric releases of toxic agents. Forward modeling methods, which describe the atmospheric transport from sources to receptors, use forward-running transport and dispersion models or computational fluid dynamics models which are run many times, and the resulting dispersion field is compared to observations from multiple sensors. Forward modeling methods include Bayesian updating and inference schemes using stochastic Monte Carlo or Markov Chain Monte Carlo sampling techniques. Backward or inverse modeling methods use only one model run in the reverse direction from the receptors to estimate the upwind sources. Inverse modeling methods include adjoint and tangent linear models, Kalman filters, and variational data assimilation, among others.This survey paper discusses these source estimation methods and lists the key references. The need for assessing uncertainties in the characterization of sources using atmospheric transport and dispersion models is emphasized.     

Long-range potential source contributions of episodic aerosol events to PM10 profile of a megacity

This paper evaluates possible long-range source contributions to the PM₁₀ profile of Istanbul, Turkey. A novel method for classifying PM₁₀ episodic events resulting from long-range transport, as opposed to local ones, was implemented. Hourly PM₁₀ mass concentrations from ten stations distributed throughout Istanbul during the year 2008 were used for this purpose. Hourly backward trajectories for the arrival of air masses to the center of Istanbul for the year 2008 were calculated using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model. Significant episodes from these backward trajectories were selected and employed in Potential Source Contribution Function (PSCF) analysis to estimate the possible contribution of long-range PM₁₀ transport (LRPMT) to observed PM₁₀ concentrations. The PSCF results showed significant seasonal variations. Based on the results obtained, PM₁₀ concentrations observed in Istanbul during summer and autumn are not heavily affected by LRPMT. Mediterranean countries, especially those of the central part of northern Africa (northern Algeria and Libya) are the most significant potential PM₁₀ contributors to Istanbul's atmosphere during springtime. During winter, Balkan countries, including the Aegean part of Turkey, Greece, Bulgaria, Serbia, and Croatia, as well as northern Italy, eastern France, southern Germany, Austria and the eastern part of Russia, were the most important LRPMT source regions for high PSCF values.     

Erratum to “Continuous four-dimensional source attribution for the Berlin area during two days in July 1994. Part I: The new Euler–Lagrange-model system LaMM5”: [Atmospheric Environment 35 (32) (2001) 5497]

The multiple nested three-dimensional (3D) mesoscale Eulerian grid point model MM5 was directly coupled with a Lagrangian particle trajectory model in order to perform a 4D source attribution for the area of Berlin based on the import probability density (IPD) distribution of the according receptor box. Within the resulting model system LaMM5 introduced here, the IPD distributions are not based on backward trajectories, which lack the recognition of the turbulent environment, but on the forward integration of a huge amount (order of million per day) of particle releases according to an emission scenario which is approximately continuous in space and time. Hence, the receptor import yields an accordingly continuous IPD distribution. Much attention has been paid on spatial and temporal resolution at the interface between both model parts (online-coupling) and the interface itself has been extended by the turbulent quantities resulting from the higher-order turbulence closure of the Eulerian model part. LaMM5 is applied on an episode with high photochemical activity across Berlin at two consecutive days (25 and 26 July 1994) with varying meteorological conditions leading to an accordingly different source attribution. The main results are:

• •The decay of the Berlin IPD with increasing source-receptor distance and time appears in an exponential manner if only sources out of a constant level (z=25 m) are regarded.
• •Heterogeneous wind fields in time and space enhance the contributions (emissions) of nearby sources to the total import of the receptor in contrast to stationary wind fields which increase the scope of the IPD distribution in upstream direction.

There are further results from several additional sensitivity studies presented in a companion paper B (Part II).     

Continuous four-dimensional source attribution for the Berlin area during two days in July 1994. Part I: the new Euler–Lagrange-model system LaMM5

The multiple nested three-dimensional mesoscale Eulerian grid point model MM5 was directly coupled with a Lagrangian particle trajectory model in order to perform a four-dimensional source attribution for the area of Berlin based on the import probability density (IPD) distribution of the according receptor box. Within the resulting model system LaMM5 introduced here, the IPD distributions are not based on backward trajectories, which lack the recognition of the turbulent environment, but on the forward integration of a huge amount (order of Million per day) of particle releases according to an emission scenario which is approximately continuous in space and time. Hence the receptor import record yields an accordingly continuous IPD distribution. Much attention has been paid on spatial and temporal resolution at the interface between both model parts (online-coupling) and the interface itself has been extended by the turbulent quantities resulting from the higher-order turbulence closure of the Eulerian model part. LaMM5 is applied on an episode with high photochemical activity across Berlin at two consecutive days (25th and 26th of July in 1994) with varying meteorological conditions leading to an accordingly different source attribution. The main results are:

• •The decay of the Berlin IPD with increasing source-receptor distance and time appears in an exponential manner if only sources out of a constant level (z=25 m) are regarded.
• •Heterogeneous wind fields in time and space enhance the contributions (emissions) of nearby sources to the total import of the receptor in contrast to stationary wind fields which increase the scope of the IPD distribution in upstream direction.

There are further results from several additional sensitivity studies presented in a companion paper B (Part II).     

Lagrangian Dispersion Modeling of Vehicular Emissions from a Highway in Complex Terrain

Abstract

Transit traffic through the Austrian Alps is of major concern in government policy. Pollutant burdens resulting from such traffic are discussed widely in Austrian politics and have already led to measures to restrict traffic on transit routes. In the course of an environmental assessment study, comprehensive measurements were performed. These included air quality observations using passive samplers, a differential optical absorption spectroscopy system, a mobile and a fixed air quality monitoring station, and meteorological observations. As was evident from several previous studies, dispersion modeling in such areas of complex terrain and, moreover, with frequent calm wind conditions, is difficult to handle. Further, in the case presented here, different pollutant sources had to be treated simultaneously (e.g., road networks, exhaust chimneys from road tunnels, and road tunnel portals). No appropriate system for modeling all these factors has so far appeared in the literature. A prognostic wind field model coupled with a Lagrangian dispersion model is thus presented here and is designed to treat all these factors. A comparison of the modeling system with results from passive samplers and from a fixed air quality monitoring station proved the ability of the model to provide reasonable figures for concentration distributions along the A10.     

Elemental carbon and sulfate aerosols over a rural mountain site in the northeastern United States: Regional emissions and implications for climate change

The effect of elemental carbon (EC) on global as well as regional climate forcing is potentially very important. However, the EC data for northeastern U.S. is sparse. Daily EC concentrations, [EC], and [SO₄] were measured in the northeastern U.S. at a regionally representative rural site, Whiteface Mountain (WFM; 44.366°N, 73.903°W, 1.5 km amsl, above mean sea level), New York (NY), for 1997. The air mass origin was determined using 6-h backward in time air trajectories obtained from the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT 4). [EC] and [SO₄] were highly variable and influenced by synoptic–scale meteorology (rainy vs dry periods). The maximum daily [EC] and [SO₄] were 364 ± 55 and 28,800 ± 3000 ng m^?3, respectively. [EC] and [SO₄] also showed seasonal variations at WFM. Occurrences of high daily [EC] were mainly in spring months, while peak daily [SO₄] concentrations occurred in summer months. This behavior of aerosols is due to the fact that the sources of EC and SO₄ are not the same and also due to the enhanced photochemical activity during summer months that increased the production of SO₄ from SO₂. High [EC] and [SO₄] values were associated with westerly air flow from the industrialized Midwestern U.S. Sector analysis using HYSPLIT 4 air trajectories showed that regions lying between the southwest and northwest of the WFM contributed 81% and 83% of the [EC] and [SO₄], respectively. The monthly net direct radiative forcing for shortwave (SW) due to EC and SO₄ aerosols at the top of the atmosphere (TOA) varied from ?0.05 to ?0.50 W m^?2, with an annual average of ?0.20 ± 0.15 W m^?2 that gives a net cooling effect. Average net radiative forcing at WFM for clear sky is lower than the global average radiative forcing reported by IPCC (Foster and Ramaswamy, 2007).     

Analysis of Air Quality Observations with the Aid of the Source-Receptor Relationship Approach

Abstract

In this study, an attempt was made to analyze time series of air quality measurements (O₃, SO₂, SO₄ ^2?NO_x) conducted at a remote place in the eastern Mediterranean (Finokalia at Crete Island in 1999) to obtain concrete information on potential contributions from emission sources. For the definition of a source-receptor relationship, advanced meteorological and dispersion models appropriate to identify “areas of influence” have been used. The model tools used are the Regional Atmospheric Modeling System and the Lagrangian-type particle dispersion model (forward and backward in time), with capabilities to derive influence functions and definition of “areas of influence.” When high levels of pollutants have been measured at the remote location of Finokalia, particles are released from this location (receptor) and traced backward in time. The influence function derived from particle distributions characterizes dispersion conditions in the atmosphere and also provides information on potential contributions from emission sources within the modeling domain to this high concentration. As was shown in the simulation results, the experimental site of Finokalia in Crete is influenced during the selected case studies, primarily by pollutants emitted from the urban conglomerate of Athens. Secondarily, it is influenced by polluted air masses arriving from Italy and/or the Black Sea Region. For some specific cases, air pollutants monitored at Finokalia were possibly related to war activities in the West Balkan Region (Kosovo).     

Particle Size Distributions and Elemental Composition of Atmospheric Particulate Matter in Southern Italy

Abstract

Three 2-wk seasonal field campaigns were performed in 2003 and 2004 at a sampling site on the southern Tyrrhenian coast of Italy with the aim to investigate the dynamics and characteristics of particle-bound pollutants in the Mediterranean area. Fine (PM_2.5) and coarse particulate matter (PM_10–2.5) size fractions were collected by a manual dichotomous sampler on 37-mm Teflon filters over a 24-hr sampling period. On average, 70% of the total PM₁₀ (PM_2.5 + PM_10–2.5) mass was associated with the coarse fraction and 30% with the fine fraction during the three campaigns. The ambient concentrations of Pb, Ni, Cr, Zn, Mn, V, Cd, Fe, Cu, Ca, and Mg associated with both size fractions were determined by atomic absorption spec-trometry. Ambient concentrations showed differences in their absolute value, ranging from few ng · m^-3 to µg ?m^-3, as well as in their variability within the PM_2.5 and PM_10–2.5 size fractions. PM₁₀ levels were well below the European Union (EU) limit value during the study period with the exception of three events during the first campaign (fall) and five events during the third campaign (spring). Two main sources were identified as the major contributors including mineral dust, transported from North Africa, and sea spray from the Tyrrhenian Sea. Comparing the results with backward trajectories, calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and Total Ozone Mapping Spectrometer-National Aeronautics and Space Administration (TOMS-NASA) maps, it was observed that in central and eastern Europe, the Tyrrhenian Sea and North Africa were the major emission source regions that affected the temporal variations and daily averages of PM_2.5 and PM_10–2.5 concentrations.     

Using prognostic model-generated meteorological output in the AERMOD dispersion model: an illustrative application in Philadelphia, PA

In this study, we introduce the prospect of using prognostic model-generated meteorological output as input to steady-state dispersion models by identifying possible advantages and disadvantages and by presenting a comparative analysis. Because output from prognostic meteorological models is now routinely available and is used for Eulerian and Lagrangian air quality modeling applications, we explore the possibility of using such data in lieu of traditional National Weather Service (NWS) data for dispersion models. We apply these data in an urban application where comparisons can be made between the two meteorological input data types. Using the U.S. Environment Protection Agency's American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD) air quality dispersion model, hourly and annual average concentrations of benzene are estimated for the Philadelphia, PA, area using both hourly MM5 model-generated meteorological output and meteorological data taken from the NWS site at the Philadelphia International Airport. Our intent is to stimulate a discussion of the relevant issues and inspire future work that examines many of the questions raised in this paper.     

A Discussion of Regulatory Requirements and Air Dispersion Modeling Approaches Applicable to U.S. Chemical Demilitarization Facilities

ABSTRACT

Owners of hazardous waste treatment, storage, and disposal facilities, and certain major air pollution sources, must conduct several separate ambient air dispersion modeling analyses before beginning construction of new facilities or modifying existing facilities. These analyses are critical components of the environmental permitting and facility certification processes and must be completed to the satisfaction of federal, state, and local regulatory authorities.

The U.S. Army has conducted air dispersion modeling for its proposed chemical agent disposal facilities to fulfill the following environmental regulatory and risk management requirements: (1) Resource Conservation and Recovery Act human health and ecological risk assessment analysis for the hazardous waste treatment and storage permit applications, (2) Quantitative Risk Assessment to support the site-specific risk management programs, and (3) Prevention of Significant Deterioration ambient air impact analysis for the air permit applications. The purpose of these air dispersion modeling studies is to show that the potential impacts on human health and the environment, due to operation of the chemical agent disposal facilities, are acceptable. This paper describes and compares the types of air dispersion models, modeling input data requirements, modeling algorithms, and approaches used to satisfy the three environmental regulatory and risk management requirements listed above. Although this paper discusses only one industry (i.e., chemical demilitarization), the information it contains could help those in other industries who need to communicate to the public the purpose and objectives of each modeling analysis. It may also be useful in integrating the results of each analysis into an overarching summary of compliance and potential risks.     

A statistical approach for estimating uncertainty in dispersion modeling: An example of application in southwestern USA

A method based on a statistical approach of estimating uncertainty in simulating the transport and dispersion of atmospheric pollutants is developed using observations and modeling results from a tracer experiment in the complex terrain of the southwestern USA. The method takes into account the compensating nature of the error components by representing all terms, except dispersion error and variance of stochastic processes. Dispersion error and the variance of the stochastic error are estimated using the maximum likelihood estimation technique applied to the equation for the fractional error. Mesoscale Model 5 (MM5) and a Lagrangian random particle dispersion model with three optional turbulence parameterizations were used as a test bed for method application. Modeled concentrations compared well with the measurements (correlation coefficients on the order of 0.8). The effects of changing two structural components (the turbulence parameterization and the model grid vertical resolution) on the magnitude of the dispersion error also were examined. The expected normalized dispersion error appears to be quite large (up to a factor of three) among model runs with various turbulence schemes. Tests with increased vertical resolution of the atmospheric model (MM5) improved most of the dispersion model statistical performance measures, but to a lesser extent compared to selection of a turbulence parameterization. Method results confirm that structural components of the dispersion model, namely turbulence parameterizations, have the most influence on the expected dispersion error.     

Identification of Sources Contributing to Mid-Atlantic Regional Aerosol

Abstract

Source types or source regions contributing to the concentration of atmospheric fine particles measured at Brigantine National Wildlife Refuge, NJ, were identified using a factor analysis model called Positive Matrix Factorization (PMF). Cluster analysis of backward air trajectories on days of high- and low-factor concentrations was used to link factors to potential source regions. Brigantine is a Class I visibility area with few local sources in the center of the eastern urban corridor and is therefore a good location to study Mid-Atlantic regional aerosol. Sulfate (expressed as ammonium sulfate) was the most abundant species, accounting for 49% of annual average fine mass. Organic compounds (22%; expressed as 1.4 × organic carbon) and ammonium nitrate (10%) were the next abundant species. Some evidence herein suggests that secondary organic aerosol formation is an important contributor to summertime regional aerosol.

Nine factors were identified that contributed to PM_2.5 mass concentrations: coal combustion factors (66%, summer and winter), sea salt factors (9%, fresh and aged), motor vehicle/mixed combustion (8%), diesel/Zn-Pb (6%), incinerator/industrial (5%), oil combustion (4%), and soil (2%). The aged sea salt concentrations were highest in springtime, when the land breeze-sea breeze cycle is strongest. Comparison of backward air trajectories of high- and low-concentration days suggests that Brigantine is surrounded by sources of oil combustion, motor vehicle/mixed combustion, and waste incinerator/industrial emissions that together account for 17% of PM_2.5 mass. The diesel/Zn-Pb factor was associated with sources north and west of Brigantine. Coal combustion factors were associated with coal-fired power plants west and southwest of the site. Particulate carbon was associated not only with oil combustion, motor vehicle/mixed combustion, waste incinerator/industrial, and diesel/Pb-Zn, but also with the coal combustion factors, perhaps through common transport.     

Modeling water exchange and contaminant transport through a Baltic coastal region

The water exchange of the Baltic coastal zone is characterized by its seasonally varying regimes. In the safety assessment of a potential repository for spent nuclear fuel, it is important to assess the consequences of a hypothetical leak of radionuclides through the seabed into a waterborne transport phase. In particular, estimates of the associated residence times in the near-shore coastal zone are of interest. There are several methods to quantify such measures, of which three are presented here. Using the coastal location of Forsmark (Sweden) as an example, methods based on passive tracers, particle trajectories, and the average age distribution of exogeneous water parcels are compared for a representative one-year cycle. Tracer-based methods can simulate diffusivity more realistically than the other methods. Trajectory-based methods can handle Lagrangian dispersion processes due to advection but neglect diffusion on the sub-grid scale. The method based on the concept of average age (AvA) of exogeneous water can include all such sources simultaneously not only boundary water bodies but also various (fresh)-water discharges. Due to the inclusion of sub-grid diffusion this method gives a smoother measure of the water renewal. It is shown that backward in time trajectories and AvA-times are basically equipollent methods, yielding correlated results within the limits set by the diffusivity.     

Distant source contributions to PM10 profile evaluated by SOM based cluster analysis of air mass trajectory sets

This paper evaluates the effects of long-range transport patterns of air masses to the regional PM profile in a megacity, Istanbul, Turkey. Five-day hourly backward trajectories were obtained by the HYSPLIT model for selected episodic events in 2008. Self Organizing Maps (SOM), a very powerful classification tool, was used to cluster these trajectories. In total, eight cluster groups were obtained. All of the clusters were evaluated with respect to inhalable particulate matter (PM₁₀) concentrations observed in Istanbul for the arrival times of the trajectories. Istanbul is generally under the effect of trajectories in three clusters (1, 2 and 4) (52% of all pre-selected episodic events), which have higher mean concentration values than the mean value of all the samples. These clusters typically make significant PM contributions to Istanbul's air quality. PM loadings of the trajectories in these clusters were attributed to massive anthropogenic activity over all of Europe and southwestern air flow most likely carrying PM₁₀ atmospheric particles originating from the Saharan Desert and other global dust generation regions located in the northern part of Africa.