Chemical composition of major VOC emission sources in the Seoul atmosphere

This paper describes a chemical analysis of volatile organic compounds (VOCs) for five emission sources in Seoul. The source categories included motor vehicle exhaust, gasoline evaporation, paint solvents, natural gas and liquefied petroleum gas (LPG). These sources were selected because they have been known to emit significant quantities of VOCs in the Seoul area (more than 5% of the total emission inventory). Chemical compositions of the five emission sources are presented for a group of 45 C2-C9 VOCs. Motor vehicle exhaust profiles were developed by conducting an urban tunnel study. These emissions profiles were distinguished from the other emission profiles by a high weight percentage of butanes over seasons and propane in the wintertime. It was found that this is due to the wide use of butane-fueled vehicles. To obtain gasoline vapor profiles, gasoline samples from five major brands for each season were selected. The brands were blended on the basis of the marketshare of these brands in Seoul area. Raoult's law was used to calculate gasoline evaporative compositions based on the liquid gasoline compositions. The measured and estimated gasoline vapor compositions were found to be in good agreement. Vehicle and gasoline evaporation profiles were made over seasons because of the seasonal change in their compositions. Paint solvent emissions profiles were produced based on a product-use survey and sales figures. These profiles are a composite of four major oil-based paints and thinning solvent. The source profile of natural gas was made on a methane-free basis. It was found that Ethane and propane were the most abundant compounds accounting for 95% of the natural gas composition. LPG was largely composed of propane and ethane and the remaining components were minor contributors.     

Comparative receptor modelling study of TSP,PM2 and PM2−10 in Ho Chi Minh City

Elemental compositions were measured for TSP (total suspended particulate matter), PM₂₋₁₀ and PM₂ (particulate matter with aerodynamic diameters from 2 to 10 μm and less than 2 μm, respectively) in Ho Chi Minh City. Concentrations of 23 elements and particulate mass (PM) were used for receptor modelling to identify and quantify aerosol sources using principal component factor analysis (PCFA). A suite of factors containing similar elements with significant factor loadings were revealed among the factor matrices, thus facilitating the identification of common sources for different aerosol types. These sources include vehicular emissions (Br and Zn), coal burning (Se), industrial processes (Ce, Co, Cr, Pb and Sb), road dust (Al, Ti, V), soil dust (Fe and Th) and biomass burning (K). Marine aerosols (Na and Cl) and mineral fly ash (Sc and La) were revealed only in the PM₂₋₁₀ model. For TSP, the last four sources are combined in one factor. The last (9th) factor in the PM₂ model, characterised by a high loading from PM and insignificant loadings from elements, was attributed to secondary sulphates and organics, although these constituents were not measured in the experiments. Such a remarkable source identification capability of the modelling technique highlights the significance of achieving an optimal factor solution as a crucial step in PCFA, that was done by systematically varying the number of factors retained and carefully evaluating each factor matrix for both model fitting performance and physical reasonableness.     

Bayesian treatment of a chemical mass balance receptor model with multiplicative error structure

The chemical mass balance (CMB) receptor model is commonly used in source apportionment studies as a means for attributing measured airborne particulate matter (PM) to its constituent emission sources. Traditionally, error terms (e.g., measurement and source profile uncertainty) associated with the model have been treated in an additive sense. In this work, however, arguments are made for the assumption of multiplicative errors, and the effects of this assumption are realized in a Bayesian probabilistic formulation which incorporates a ‘modified’ receptor model. One practical, beneficial effect of the multiplicative error assumption is that it automatically precludes the possibility of negative source contributions, without requiring additional constraints on the problem. The present Bayesian treatment further differs from traditional approaches in that the source profiles are inferred alongside the source contributions. Existing knowledge regarding the source profiles is incorporated as prior information to be updated through the Bayesian inferential scheme. Hundreds of parameters are therefore present in the expression for the joint probability of the source contributions and profiles (the posterior probability density function, or PDF), whose domain is explored efficiently using the Hamiltonian Markov chain Monte Carlo method. The overall methodology is evaluated and results compared to the US Environmental Protection Agency's standard CMB model using a test case based on PM data from Fresno, California.     

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.     

Review of receptor model fundamentals

There are several broad classes of mathematical models used to apportion the aerosol measured at a receptor site to its likely sources. This paper surveys the two types applied in exercises for the Mathematical and Empirical Receptor Models Workshop (Quail Roost II): chemical mass balance models and multivariate models. The fundamental principles of each are reviewed. Also considered are the specific models available within each class. These include: tracer element, linear programming, ordinary linear least-squares, effective variance least-squares and ridge regression (all solutions to the chemical mass balance equation), and factor analysis, target transformation factor analysis, multiple linear regression and extended Q-mode factor analysis (all multivariate models). In practical application of chemical mass balance models, a frequent problem is the presence of two or more emission sources whose signatures are very similar. Several techniques to reduce the effects of such multicollinearity are discussed. The propagation of errors for source contribution estimates, another practical concern, also is given special attention.     

Optimized variable source-profile approach for source apportionment

An expanded chemical mass balance (CMB) approach for PM_2.5 source apportionment is presented in which both the local source compositions and corresponding contributions are determined from ambient measurements and initial estimates of source compositions using a global-optimization mechanism. Such an approach can serve as an alternative to using predetermined (measured) source profiles, as traditionally used in CMB applications, which are not always representative of the region and/or time period of interest. Constraints based on ranges of typical source profiles are used to ensure that the compositions identified are representative of sources and are less ambiguous than the factors/sources identified by typical factor analysis (FA) techniques. Gas-phase data (SO₂, CO and NO_y) are also used, as these data can assist in identifying sources. Impacts of identified sources are then quantified by minimizing the weighted-error between apportioned and measured levels of the fitting species. This technique was applied to a dataset of PM_2.5 measurements at the former Atlanta Supersite (Jefferson Street site), to apportion PM_2.5 mass into nine source categories. Good agreement is found when these source impacts are compared with those derived based on measured source profiles as well as those derived using a current FA technique, Positive Matrix Factorization. The proposed method can be used to assess the representativeness of measured source-profiles and to help identify those profiles that may be in significant error, as well as to quantify uncertainties in source-impact estimates, due in part to uncertainties in source compositions.     

Land Use-related Chemical Composition of Street Sediments in Beijing

BACKGROUND: More than 10 million people are currently living in Beijing. This city faces severe anthropogenic air pollution caused by an intense vehicle increase (11% per year in China), coal combusting power plants, heavy industry, huge numbers of household and restaurant cookers, and domestic heating stoves. Additionally, each year dust storms are carrying particulate matter from the deserts of Gobi and Takla Makan towards Beijing, especially in spring. Other geogenic sources of particulate matter which contribute to the air pollution are bare soils, coal heaps and construction sites occurring in and around Beijing. Streets function as receptor surfaces for atmospheric dusts. Thus, street sediments consist of particles of different chemical compositions from many different sources, such as traffic, road side soils and industry. METHODS: Distributions and concentrations of various chemical elements in street sediments were investigated along a rural-urban transect in Beijing, China. Chemical elements were determined with X-ray fluorescence analysis. Factor analysis was used to extract most important element sources contributing to particulate pollution along a main arterial route of the Chinese capital. RESULTS AND DISCUSSION: The statistical evaluation of the data by factor analysis identifies three main anthropogenic sources responsible for the contamination of Beijing street sediments. The first source is a steel factory in the western part of Beijing. From this source, Mn, Fe, and Ti were emitted into the atmosphere through chimneys and by wind from coal heaps used as the primary energy source for the factory. The second source is a combination of traffic, domestic heating and some small factories in the center of Beijing discharging Cu, Pb, Zn and Sn. Calcium and Cr characterize a third anthropogenic element source of construction materials such as concrete and mortar. Beside the anthropogenic contamination, some elements like Y, Zr, Nb, Ce, and Rb are mainly derived from natural soils and from the deserts. This is supported by mineral phase analysis, which showed a clear imprint of material in road dusts coming from the West-China deserts. CONCLUSIONS: Our results clearly show that the chemical composition of urban road dusts can be used to identify distinct sources responsible for their contamination. The study demonstrates that the chemistry of road dusts is an important monitor to assess the contamination in the urban environment. Chemical composition of street sediments in Beijing comprises the information of different sources of atmospheric particles. RECOMMENDATIONS AND OUTLOOK: This study is only a small contribution to the understanding of substance fluxes related to Beijing's dust. More effort is required to assess Beijing's dust fluxes, since the dust harms the living quality of the inhabitants. Especially the measurable superimposing of long scale transported dust from dry regions with the anthropogenic polluted urban dust makes investigations of Beijing's dust scientifically valuable.     

Considerations for design of source apportionment studies

This report recommends procedures for source and ambient sampling and analysis in source apportionment studies. The recommendations are based on the results of receptor model studies of atmospheric particles in urban areas, especially a recent study of Houston, TX, undertaken as part of the Mathematical and Empirical Receptor Models Workshop (Quail Roost II). The recommendations are presented at three levels of increasing cost and detail of information obtained. Existing mass emissions inventories combined with chemically resolved test data from similar sources (not necessarily in the same locale) can be used to initially estimate the sources of elements present on ambient particles. To aid local users in construction of chemically resolved emission estimates, the U.S. Environmental Protection Agency (EPA) is compiling a library of compositions and size distributions of particulate emissions from major source types. More reliable source characterization can be achieved if the actual sources are tested directly. EPA should develop and publish detailed procedures for source sampling that would be more appropriate for receptor model use than are existing standard methods. Source and ambient sampling should be conducted by similar methods. If possible, particles from sources should be collected in a way that simulates changes that would normally occur before they reach distant receptors (e.g. by diluting and cooling the particles from hot sources). It is recommended that particulate samples be routinely collected in two size fractions by use of virtual impactors and that all samples be subjected, at a minimum, to mass and X-ray fluorescence analyses. Additional measurements are suggested for obtaining more detailed information: neutron activation analysis; X-ray diffraction; automated particle classification by electron microscopy; analyses for classes of organic species, ¹⁴C and thermally released carbonaceous species; and real-time observation of several gases during sample collection. Methods for collecting meteorological data in parallel with ambient samples are described, as are methods for incorporating such data into the source identification process.     

Aerosol composition and sources in Urban areas in India

The elemental composition of aerosols over four cities in India was determined by EDXRF over a 1-year period and the results are presented. The crustal element levels in air in all the cities are found to be similar to those reported for urban areas elsewhere in the world while that of anthropogenic elements are lower. The aerosol compositions show a seasonal variation. The levels of crustal elements are higher during summer and decrease with rainfall. The anthropogenic elements are generally higher during winter. The lead levels are essentially from non-auto exhaust sources such as refuse burning, smelter etc., and in three of the cities they are strongly correlated to zinc. The crustal elements are from local soils and no significant crust-air fractionation is seen. Factor analysis of the data suggests between four and six aerosol source types for the four cities. Soil dust accounts for the major fraction of the measured mass. About 68–80 % of the aerosol mass comprising the residuals seems to consist of C, O₂ and N, in particular, which were not measured.     

Examining intra-urban variation in fine particle mass constituents using GIS and constrained factor analysis

Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM_2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO₂) and PM_2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM_2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM_2.5 source contributions can be partially predicted using GIS-based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies.     

Speciation and Characterization of Heavy Metal-Contaminated Soils Using Computer-Controlled Scanning Electron Microscopy

In the analysis of heavy metal-contaminated soils, particle-by-particle determination of elemental composition (i.e. species or mineral) as well as the characterization of particle size, external and internal morphology, can be used to assess bioavailability, remediation potential and source. The imaging capabilities of the scanning electron microscope (SEM) coupled with the elemental analysis provided by energy dispersive spectroscopy (EDS) can be used to acquire both chemical and physical information. Heavy metal particles can be analyzed automatically using computer-controlled scanning electron microscopy (CCSEM). The analysis first recognizes a particle and determines is periphery and various size measures. An EDS spectrum is obtained, the constituent elements identified and the occurrence classified into a compositional type. A TIFF microimage with spectrum is saved for later off-line review and location coordinates are saved for on-line review.Two examples illustrate the use of this technique in source identification. In one example, elevated levels of arsenic were attributed to a manufactured product rather than a fugitive source. In a second example, the lead-bearing constituents observed, as well as an association with a vuggy aluminosilicate occurrence and unburned coal, identifies coal ash as the probable source.     

Testing and optimizing two factor-analysis techniques on aerosol at Narragansett,Rhode Island

Elemental data for aerosol at Narragansett, RI, USA, were used to compare the source-identification power of positive matrix factorization (PMF), a new variant of factor analysis, with that of conventional factor analysis (CFA) and to investigate how much each technique can be “tuned” for best results. The techniques generally yielded similar results. Although both were degraded by weak elements and gave factors that always differed somewhat from known sources, they nonetheless provided substantial insight into sources of elements. PMF was harder to use than CFA but resolved crustal and marine components up to an order of magnitude better. Best results were generally obtained when the data were log-transformed, when missing data were replaced by means, and when various numbers of factors were tried and their results carefully evaluated for physical reasonableness. But the most important consideration was found to be the choice of elements, which outweighed all differences between techniques. Therefore, to maximize the source-identification power of factor analysis, the two most important steps appear to be selecting the optimum set of elements and selecting the basic technique, in that order.     

Source Apportionment of Carbonaceous Aerosol in New York City by Multiple Linear Regression

A multiple linear regression model was applied to aerosol chemical data from New York City to determine the sources of carbonaceous aerosol. The model used elemental tracers for auto exhaust aerosol (Pb), residual oil combustion (V), resuspended dust (Mn or Fe), and incineration (Cu or Zn). Although relative uncertainties in the source apportionment were greater than 20%, auto exhaust was found to be the main source of organic carbon with lesser contributions from other sources. A substantial fraction of elemental carbon could not be associated with the sources used in the model and was possibly associated with the combustion of diesel and distillate oils. The regression coefficients, which are related to source composition, compared well with actual measured source compositions. Because of the uncertainties it was concluded that source apportionment, especially as it relates to the development of control strategies, should utilize the results of several receptor and source models where possible.     

An Efficient Gaussian-Plume Multiple-Source Air Quality Algorithm

The information presented in this paper is directed to air pollution scientists with an interest in applying air quality simulation models. RAM is the three letter designation for this efficient Gaussian-plume multiple-source air quality algorithm. RAM is a method of estimating short-term dispersion using the Gaussian steady-state model. This algorithm can be used for estimating air quality concentrations of relatively stable pollutants for averaging times from an hour to a day in urban areas from point and area sources. The algorithm is applicable for locations with level or gently rolling terrain where a single wind vector for each hour is a good approximation to the flow over the source area considered. Calculations are performed for each hour. Hourly meteorological data required are wind direction, wind speed, stability class, and mixing height. Emission information required of point sources consists of source coordinates, emission rate, physical height, stack gas volume flow and stack gas temperature. Emission information required of area sources consists of south-west corner coordinates, source area, total area emission rate and effective area source height. Computation time is kept to a minimum by the manner in which concentrations from area sources are estimated using a narrow plume hypothesis and using the area source squares as given rather than breaking down all sources to an area of uniform elements. Options are available to the user to allow use of three different types of receptor locations: 1 ) those whose coordinates are input by the user, 2) those whose coordinates are determined by thé model and are downwind óf significant point and area sources where maxima are likely to occur, and 3) those whose coordinates are determined by the model to give good area coverage of a specific portion of the region. Computation time is also decreased by keeping the number of receptors to a minimum.     

Provenances of atmospheric dust over Korea from Sr–Nd isotopes and rare earth elements in early 2006

Sr and Nd isotopic composition of pre- and syn-Asian dust (Hwangsa) particles collected from three different water depths at two different offshore sites, western Korea and rare earth elemental composition of syn-Asian dust particles collected from three islands around the Korean Peninsula in late April 2006 were analyzed to interpret their provenance. The dust Sr–Nd isotopic compositions vary spatiotemporally, but they show specific values when the Hwangsa event occurred. Satellite images, airmass backward trajectory modeling, and comparison with Sr–Nd isotopic ratios and rare earth elements compositions of soils and desert sands of northern China all suggest the major source of dust particles for the late April 2006 Hwangsa event to be the Mu Us Desert in northern China. Dust particles of the pre-Hwangsa period include both background dusts and the previous Hwangsa event dust particles, and they are interpreted to have been originated from various arid regions of China such as the Hobq Desert, the Mu Us Desert, and the Taklamakan Desert in different times. Different background dust sources during pre-Hwangsa period in early 2006 resulted from the changing route of the westerlies.     

Review of receptor modeling methods for source apportionment

Efforts have been made to relate measured concentrations of airborne constituents to their origins for more than 50 years. During this time interval, there have been developments in the measurement technology to gather highly time-resolved, detailed chemical compositional data. Similarly, the improvements in computers have permitted a parallel development of data analysis tools that permit the extraction of information from these data. There is now a substantial capability to provide useful insights into the sources of pollutants and their atmospheric processing that can help inform air quality management options. Efforts have been made to combine receptor and chemical transport models to provide improved apportionments. Tools are available to utilize limited numbers of known profiles with the ambient data to obtain more accurate apportionments for targeted sources. In addition, tools are in place to allow more advanced models to be fitted to the data based on conceptual models of the nature of the sources and the sampling/analytical approach. Each of the approaches has its strengths and weaknesses. However, the field as a whole suffers from a lack of measurements of source emission compositions. There has not been an active effort to develop source profiles for stationary sources for a long time, and with many significant sources built in developing countries, the lack of local profiles is a serious problem in effective source apportionment. The field is now relatively mature in terms of its methods and its ability to adapt to new measurement technologies, so that we can be assured of a high likelihood of extracting the maximal information from the collected data.

Implications: Efforts have been made over the past 50 years to use air quality data to estimate the influence of air pollution sources. These methods are now relatively mature and many are readily accessible through publically available software. This review examines the development of receptor models and the current state of the art in extracting source identification and apportionments from ambient air quality data.     

Refining emission rate estimates using a coupled receptor–dispersion modeling approach

Receptor modeling techniques like chemical mass balance are used to attribute pollution levels at a point to different sources. Here we analyze the composition of particulate matter and use the source profiles of sources prevalent in a region to estimate quantitative source contributions. In dispersion modeling on the other hand the emission rates of various sources together with meteorological conditions are used to determine the concentrations levels at a point or in a region. The predictions using these two approaches are often inconsistent. In this work these differences are attributed to errors in emission inventory. Here an algorithm for coupling receptor and dispersion models is proposed to reduce the differences of the two predictions and determine the emission rates accurately. The proposed combined approach helps reconcile the differences arising when the two approaches are used in a stand-alone mode. This work is based on assuming that the models are perfect and uses a model-to-model comparison to illustrate the concept.     

Use of Stable Isotopes to Identify Sources of Mercury in Sediments: A Review and Uncertainty Analysis

Mass-dependent and mass-independent mercury isotope fractionation potentially generates unique source signatures that can be used to apportion contributions to sediment contamination. This article reviews findings from previous investigations that have used mercury isotopes to identify sources. It also discusses a mass balance mercury isotope fractionation model that simulates changes in isotopic source signatures in aquatic systems caused by natural biogeochemical cycling. According to the model, the extent of source signature alteration depends on chemical speciation, with more labile forms exhibiting greater isotopic fractionation. Apportionment is tractable when differences between δ²⁰²Hg of sources are larger than potential changes in isotopic signatures following the release of mercury into the environment.     

Long-term changes in elemental deposition at the earth's surface

The rate of deposition of elements at a point on the earth's surface can change, quite dramatically, even on relatively short time-scales, as a function of weather patterns. On a global scale volcanoes (and more rarely large meteors) can overwhelm steadier sources of trace elements. In recent centuries human activities have increased emissions to the atmosphere to a point where they are above those of natural sources for some of the rarer elements. On a longer time-scale climate change can also alter the deposition of elements, although such changes are often slower than those brought about through anthropogenic emissions. Changes in climate can also alter the distribution of deposition, but there are few studies estimating such changes. This paper uses estimates of the balance of natural and anthropogenic sources of a range of elements to examine the likely variation in deposition at the earth's surface. It particularly focuses on those elements regarded as toxic, whose concentrations seem likely to go on increasing in industrial areas.     

Nitrogen isotopes: Tracers of origin and processes affecting PM10 in the atmosphere of Paris

Nitrogen in atmospheric particles in an urban environment is the result of complex primary and secondary processes, which renders identifying its origin somewhat complicated. Using the example of PM₁₀ in the atmosphere of Paris (France), it is shown that the use of stable nitrogen-isotope compositions (δ¹⁵N) alleviates this difficulty and provides clear information on the sources of primary and possibly of secondary nitrogen. Characterization of emissions of the different types of emitters in the city (road traffic, waste incinerators and heating sources) shows that these are clearly discriminated by specific isotope signatures. δ¹⁵N is particularly useful in showing that a substantial portion of the nitrogen is the result of secondary reactions, reactions that are different in summer and winter, as are the corresponding pollution sources. While it is unclear, among point sources, what the winter source of primary nitrogen is, road traffic appear to be the source of primary nitrogen in summer. Identification of the sources of the secondary nitrogen strongly depends on the nitrogen isotope fractionations (Δ¹⁵N) associated to atmospheric conversion of NO_x to nitrate, but hypothesises presented here hint at the possible corresponding pollution sources.