Comparison of the MURA and an improved single-receptor (SIRA) trajectory source apportionment (TSA) method using artificial sources

Two trajectory source apportionment methods were tested using an artificially generated data set to determine their ability to detect the known sources. The forward-looking step from the multi-receptor trajectory analysis (MURA) method was added to the conditional probability (CP)method of Ashbaugh et al. [1985. A residence time probability analysis of sulfur concentrations at Grand Canyon National Park. Atmospheric Environment 19(8), 1263–1270] to develop the single-receptor forward CP (SIRA) method. The multi-receptor (MURA) and the SIRA methods were tested with three simulations using artificially generated sources. The ability of the methods to detect the sources was quantified for each simulation. The first simulation showed that the SIRA method is an improvement over the original CP method. The MURA trajectory method proved to be superior at identifying sources for the simulation located in the west and comparable to the SIRA method for the two simulations located in the east.     

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.     

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.     

Fine particulate source apportionment using data from the USEPA speciation trends network in Chicago,Illinois: Comparison of two source apportionment models

Data from two of the United States Environmental Protection Agency's speciation trends network fine particulate matter sites within Chicago, Illinois were analyzed using the chemical mass balance (CMB) and positive matrix factorization (PMF) models to determine source contributions to the ambient fine particulate concentrations. The results from the two models were compared to determine the similarities and differences in the source contributions. This included examining the differences in the magnitude of the individual source contributions as well as the correlation between the contribution values from the two methods. The results showed that both models predicted sulfates, nitrates and motor vehicles as the three highest fine particle contributors for the two sites accounting for approximately 80% of the total. The PMF model attributed a slightly greater amount of fine particulate to the road salt, steel and soil sources while vegetative burning contributed more in the CMB results. Correlations between the contribution results from the two models were high for sulfates, nitrates and road salt with very good correlations existing for motor vehicles and petroleum refineries. The predicted PMF profiles agreed well with measured source profiles for the major species associated with each source.     

Comparison of the results obtained by four receptor modelling methods in aerosol source apportionment studies

In this work the performance and theoretical background behind two of the most commonly used receptor modelling methods in aerosol science, principal components analysis (PCA) and positive matrix factorization (PMF), as well as multivariate curve resolution by alternating least squares (MCR-ALS) and weighted alternating least squares (MCR-WALS), are examined. The performance of the four methods was initially evaluated under standard operational conditions, and modifications regarding data pre-treatment were then included. The methods were applied using raw and scaled data, with and without uncertainty estimations. Strong similarities were found among the sources identified by PMF and MCR-WALS (weighted models), whereas discrepancies were obtained with MCR-ALS (unweighted model). Weighting of input data by means of uncertainty estimates was found to be essential to obtain robust and accurate factor identification. The use of scaled (as opposed to raw) data highlighted the contribution of trace elements to the compositional profiles, which was key to the correct interpretation of the nature of the sources. Our results validate the performance of MCR-WALS for aerosol pollution studies.     

Characterisation and source apportionment of fine particulate sources at Hanoi from 2001 to 2008

PM_2.5 particulate matter has been collected on Teflon filters every Sunday and Wednesday at Hanoi, Vietnam for nearly eight years from April 2001 to December 2008. These filters have been analysed for over 21 different chemical species from hydrogen to lead by ion beam analysis techniques. This is the first long term PM_2.5 dataset for this region. The average PM_2.5 mass for the study period was (54 ± 33) μg m^?3, well above the current US EPA health goal of 15 μg m^?3. The average PM_2.5 composition was found to be (29 ± 8)% ammonium sulfate, (8.9 ± 3.3)% soil, (28 ± 11)% organic matter, (0.6 ± 1.4)% salt and (9.2 ± 2.8)% black carbon. The remaining missing mass (25%) was mainly nitrates and absorbed water. Positive matrix factorisation techniques identified the major source contributions to the fine mass as automobiles and transport (40 ± 10)%, windblown soil (3.4 ± 2)%, secondary sulfates (7.8 ± 10)%, smoke from biomass burning (13 ± 6)%, ferrous and cement industries (19 ± 8)%, and coal combustion (17 ± 7)% during the 8 year study period.     

宁波市大气可吸入颗粒物PM1o和PM2.5的源解析研究

在宁波市布设4个代表性点位,于2010年春季、夏季和冬季进行大气PM10和PM2.s的采样,同时采集了多种颗粒物源样品,建立了PM10、PM2.5和源样品的化学成分谱.采用化学质量平衡模型(CMB)对宁波市PM10、PM2.5进行源解析.结果表明,城市扬尘、煤烟尘、机动车尾气尘是宁波市PM10、PM2.5的3大污染源,...     

Monsoon rain chemistry and source apportionment using receptor modeling in and around National Capital Region (NCR) of Delhi,India

Studies on monsoon precipitation chemistry were carried out to understand the nature of rainwater and sources of pollution at eight different locations in and around the National Capital Region (NCR) of Delhi during southwest monsoon in the years 2003–2005. These sites were Bulandshahr (BUL), Garhmuktesar (GAR), Muradnagar (MUR), Sardhana (SAR), Panipat (PAN), Charkhi Dadri (CHA), Hodal (HOD) and Bahror (BAH). The rainwater samples, collected at these locations, were analyzed for major anions, cations and pH. The data were assessed for its quality. In general, the order of concentrations of major ions was observed to be: Ca²⁺>SO₄²⁻>HCO₃⁻>NH₄⁺>Cl⁻>NO₃⁻>Na⁺>Mg²⁺>K⁺>F⁻. The average pH of rainwater at these stations was observed to be 6.39, ranging from 5.77 to 6.62, indicating alkaline nature. However, a few rain events, 31% at Panipat, 12% at Muradnagar and 29% at Sardhana, were observed to be acidic (pH<5.6). Acidity observed at Panipat and Muradnagar is attributed to industrial influence but at Sardhana to weak organic acids contributed by surrounding vegetation. No definite trends are found for most of the components at all the sites. However, concentrations of Cl⁻ at Bulandshahr; NO₃⁻ at Muradnagar and Hodal; F⁻ at Panipat and Bahror; Na⁺ at Bulandshahr; K⁺ at Bahror and Bulandshahr; Ca²⁺ at Bulandshahr, Muradnagar, Charkhi Dadri and Sardhana; Mg²⁺ at Panipat, Bahror, Bulandshahr, Hodal and Sardhana have increased from 2003 to 2005. For source identification, principal component analysis (PCA) was made, which showed that in general, at all the sites; suspended soil–dust and sea salts which are natural sources, were identified as the most dominating. Sources like agriculture including cattle, brick kilns and industries were reflected in third or fourth PC indicating moderate influence of anthropogenic activities in this region.     

Multiple-year black carbon measurements and source apportionment using delta-C in Rochester, New York

Black carbon (BC), an important component ofthe atmospheric aerosol, has climatic, environmental, and human health significance. In this study, BC was continuously measured using a two-wavelength aethalometer (370 nm and 880 nm) in Rochester; New York, from January 2007 to December 2010. The monitoring site is adjacent to two major urban highways (I-490 and I-590), where 14% to 21% of the total traffic was heavy-duty diesel vehicles. The annual average BC concentrations were 0.76 microg/m3, 0.67 microg/m3, 0.60 microg/m3, and 0.52 microg/m3 in 2007, 2008, 2009, and 2010, respectively. Positive matrix factorization (PMF) modeling was performed using PM2.5 elements, sulfate, nitrate, ammonia, elemental carbon (EC), and organic carbon (OC) data from the US. Environmental Protection Agency (EPA) speciation network and Delta-C (UVBC370nm-BC880nm) data. Delta-C has been previously shown to be a tracer of wood combustion factor It was used as an input variable in source apportionment models for the first time in this study and was found to play an important role in separating traffic (especially diesel) emissions from wood combustion emissions. The result showed the annual average PM2.5 concentrations apportioned to diesel emissions in 2007, 2008, 2009, and 2010 were 1.34 microg/m3, 1.25 microg/m3, 1.13 microg/m3, and 0.97 microg/m3, respectively. The BC conditional probability function (CPF) plots show a large contribution from the highway diesel traffic to elevated BC concentrations. The measurements and modeling results suggest an impact of the US Environmental Protection Agency (EPA) 2007 Heavy-Duty Highway Rule on the decrease ofBC and PM2.5 concentrations during the study period.     

Multiple-year black carbon measurements and source apportionment using Delta-C in Rochester,New York

Black carbon (BC), an important component of the atmospheric aerosol, has climatic, environmental, and human health significance. In this study, BC was continuously measured using a two-wavelength aethalometer (370 nm and 880 nm) in Rochester, New York, from January 2007 to December 2010. The monitoring site is adjacent to two major urban highways (I-490 and I-590), where 14% to 21% of the total traffic was heavy-duty diesel vehicles. The annual average BC concentrations were 0.76 μg/m³, 0.67 μg/m³, 0.60 μg/m³, and 0.52 μg/m³ in 2007, 2008, 2009, and 2010, respectively. Positive matrix factorization (PMF) modeling was performed using PM_2.5 elements, sulfate, nitrate, ammonia, elemental carbon (EC), and organic carbon (OC) data from the U.S. Environmental Protection Agency (EPA) speciation network and Delta-C (UVBC_370nm – BC_880nm) data. Delta-C has been previously shown to be a tracer of wood combustion factor. It was used as an input variable in source apportionment models for the first time in this study and was found to play an important role in separating traffic (especially diesel) emissions from wood combustion emissions. The result showed the annual average PM_2.5 concentrations apportioned to diesel emissions in 2007, 2008, 2009, and 2010 were 1.34 μg/m³, 1.25 μg/m³, 1.13 μg/m³, and 0.97 μg/m³, respectively. The BC conditional probability function (CPF) plots show a large contribution from the highway diesel traffic to elevated BC concentrations. The measurements and modeling results suggest an impact of the U.S Environmental Protection Agency (EPA) 2007 Heavy-Duty Highway Rule on the decrease of BC and PM_2.5 concentrations during the study period.

Implications: This study suggests that there was an observable impact of the U.S EPA 2007 Heavy-Duty Highway Rule on the ambient black carbon concentrations in Rochester, New York. Aethalometer Delta-C was used as an input variable in source apportionment models and it allowed the separation of traffic (especially diesel) emissions from wood combustion emissions.     

Evaluation of aerosol sources at European high altitude background sites with trajectory statistical methods

This study has investigated the influence of synoptic weather patterns and long-range transport episodes on the concentrations of several compounds related to different aerosol sources (EC, OC, SO₄^2?, Ca²⁺, Na⁺, K⁺, ²¹⁰Pb, levoglucosan and dicarboxylic acids) registered in PM10 or PM2.5 aerosol samples collected at three remote background sites in central Europe. Air mass back-trajectories arriving at these sites have been analysed by statistical methods. Firstly, air mass back-trajectories have been grouped into clusters. Each cluster corresponds to specific meteorological scenarios, which were extracted and discussed. Finally, redistributed concentration fields have been computed to identify the main potential source regions of the different key aerosol components. A marked seasonal pattern is observed in the occurrence of the different clusters, with fast westerly and northerly Atlantic flows during winter and weak circulation flows in summer. Spring and fall were characterised by advection of moderate flows from northeastern and eastern Europe. Significant inter-cluster differences were observed for concentrations of receptor aerosol components, with the highest concentrations of EC, OC, SO₄^2?, K⁺ and ²¹⁰Pb associated with local and mesoscale aerosol sources located over central Europe related to enhanced photochemical processes. Emissions produced by fossil fuel and biomass burning processes from the Baltic countries, Byelorussia, western regions of Russia and Kazakhstan in spring and fall also contribute to elevated levels of EC, OC, SO₄^2?, K⁺ and ²¹⁰Pb. In the summer period long-range transport episodes of mineral dust from North-African deserts were also frequently detected, which caused elevated concentrations of coarse Ca²⁺ at sites. The baseline aerosol concentrations in central Europe at the high altitude background sites were registered in winter, with the exception of coarse Na⁺. While the relatively high concentrations of Na⁺ can be explained by sea salt advected from the Atlantic, the low levels of other aerosol components are caused by efficient aerosol scavenging associated to advections of Atlantic air masses, as well as lower emissions of these species over the Atlantic compared to those over the European continent and very limited vertical air mass exchange over the continent.     

Comparison of receptor models for source apportionment of volatile organic compounds in Beijing, China

Identifying the sources of volatile organic compounds (VOCs) is key to reducing ground-level ozone and secondary organic aerosols (SOAs). Several receptor models have been developed to apportion sources, but an intercomparison of these models had not been performed for VOCs in China. In the present study, we compared VOC sources based on chemical mass balance (CMB), UNMIX, and positive matrix factorization (PMF) models. Gasoline-related sources, petrochemical production, and liquefied petroleum gas (LPG) were identified by all three models as the major contributors, with UNMIX and PMF producing quite similar results. The contributions of gasoline-related sources and LPG estimated by the CMB model were higher, and petrochemical emissions were lower than in the UNMIX and PMF results, possibly because the VOC profiles used in the CMB model were for fresh emissions and the profiles extracted from ambient measurements by the two-factor analysis models were "aged".     

Effects of soil dust episodes and mixed fuel sources on source apportionment of PM10 particles in Kuopio,Finland

A receptor modeling study was carried out in Kuopio, Finland, between January and April 1994. Near the center of town, the daily mean concentrations were measured for PM10, sulphur dioxide, carbon monoxide and Black Smoke. Elemental concentrations of PM10 samples for 38 days were analyzed by ICP-MS. The main sources and their contributions to the measured concentrations of PM10 particles were solved by receptor modeling using a factor analysis-multiple linear regression (FA-MLR) model. Because a dust episode was very strong during two sampling days, the FA analysis was strongly influenced by this episode and did not give main factors. The factor analysis, when the two episode days were omitted, gave credible factors related to the sources in the study area. The four major sources and their estimated contributions to the average PM10 concentration of 27.2 μg m^-3 were: soil and street dust 46–48%, heavy fuel oil burning 12–18%, traffic exhaust 10–14%, wood burning ca. 11% and unidentified sources 15–25%. However, during spring dust episode days, with maximum PM10 concentration of 150 μg m^-3, the main source of PM10 was soil.     

Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM2.5 in Guangzhou, southern China, using positive matrix factorization (PMF)

From 28 November to 23 December 2009, 24-h?PM_2.5 samples were collected simultaneously at six sites in Guangzhou. Concentrations of 18 polycyclic aromatic hydrocarbons (PAHs) together with certain molecular tracers for vehicular emissions (i.e., hopanes and elemental carbon), coal combustion (i.e., picene), and biomass burning (i.e., levoglucosan) were determined. Positive matrix factorization (PMF) receptor model combined with tracer data was applied to explore the source contributions to PAHs. Three sources were identified by both inspecting the dominant tracer(s) in each factor and comparing source profiles derived from PMF with determined profiles in Guangzhou or in the Pearl River Delta region. The three sources identified were vehicular emissions (VE), biomass burning (BB), and coal combustion (CC), accounting for 11?±?2 %, 31?±?4 %, and 58?±?4 % of the total PAHs, respectively. CC replaced VE to become the most important source of PAHs in Guangzhou, reflecting the effective control of VE in recent years. The three sources had different contributions to PAHs with different ring sizes, with higher BB contributions (75?±?3 %) to four-ring PAHs such as pyrene and higher CC contributions (57?±?4 %) to six-ring PAHs such as benzo[ghi]perylene. Temporal variations of VE and CC contributions were probably caused by the change of weather conditions, while temporal variations of BB contributions were additionally influenced by the fluctuation of BB emissions. Source contributions also showed some spatial variations, probably due to the source emission variations near the sampling sites.     

Quantification and radiocarbon source apportionment of black carbon in atmospheric aerosols using the CTO-375 method

To make progress towards linking the atmosphere and biogeosphere parts of the black carbon (BC) cycle, a chemothermal oxidation method (CTO-375), commonly applied for isolating BC from complex geomatrices such as soils, sediments and aquatic particles, was applied to investigate the BC also in atmospheric particles. Concentrations and ¹⁴C-based source apportionment of CTO-375 based BC was established for a reference aerosol (NIST RM-8785) and for wintertime aerosols collected in Stockholm and in a Swedish background area. The results were compared with thermal–optical (OC/EC) measurements. For NIST RM-8785, a good agreement was found between the BC_CTO-375 concentration and the reported elemental carbon (EC) concentration measured by the “Speciation Trends Network—National Institute of Occupational Safety and Health” method (EC_NIOSH) with BC_CTO-375 of 0.054±0.002 g g⁻¹ and EC_NIOSH of 0.067±0.008 g g⁻¹. In contrast, there was an average factor of ca. 20 difference between BC_CTO-375 and EC_NIOSH for the ambient Scandinavian wintertime aerosols, presumably reflecting a combination of BC_CTO-375 isolating only the recalcitrant soot-BC portion of the BC continuum and the EC_NIOSH metric inadvertently including some intrinsically non-pyrogenic organic matter. Isolation of BC_CTO-375 with subsequent off-line radiocarbon analysis yielded fraction modern values (fM) for total organic carbon (TOC) of 0.93 (aerosols from a Swedish background area), and 0.58 (aerosols collected in Stockholm); whereas the fM for BC_CTO-375 isolates were 1.08 (aerosols from a Swedish background area), and 0.87 (aerosols collected in Stockholm). This radiocarbon-based source apportionment suggests that contribution from biomass combustion to cold-season atmospheric BC_CTO-375 in Stockholm was 70% and in the background area 88%.     

PM2.5 source apportionment with organic markers in the Southeastern Aerosol Research and Characterization (SEARCH) study

Positive matrix factorization (PMF) and effective variance (EV) solutions to the chemical mass balance (CMB) were applied to PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm) mass and chemically speciated measurements for samples taken from 2008 to 2010 at the Atlanta, Georgia, and Birmingham, Alabama, sites. Commonly measured PM_2.5 mass, elemental, ionic, and thermal carbon fraction concentrations were supplemented with detailed nonpolar organic speciation by thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS). Source contribution estimates were calculated for motor vehicle exhaust, biomass burning, cooking, coal-fired power plants, road dust, vegetative detritus, and secondary sulfates and nitrates for Atlanta. Similar sources were found for Birmingham, with the addition of an industrial source and the separation of biomass burning into open burning and residential wood combustion. EV-CMB results based on conventional species were qualitatively similar to those estimated by PMF-CMB. Secondary ammonium sulfate was the largest contributor, accounting for 27–38% of PM_2.5, followed by biomass burning (21–24%) and motor vehicle exhaust (9–24%) at both sites, with 4–6% of PM_2.5 attributed to coal-fired power plants by EV-CMB. Including organic compounds in the EV-CMB reduced the motor vehicle exhaust and biomass burning contributions at both sites, with a 13–23% deficit for PM_2.5 mass. The PMF-CMB solution showed mixing of sources within the derived factors, both with and without the addition of speciated organics, as is often the case with complex source mixtures such as those at these urban-scale sites. The nonpolar TD-GC/MS compounds can be obtained from existing filter samples and are a useful complement to the elements, ions, and carbon fractions. However, they should be supplemented with other methods, such as TD-GC/MS on derivitized samples, to obtain a wider range of polar compounds such as sterols, sugars, and organic acids. The PMF and EV solutions to the CMB equations are complementary to, rather than replacements for, each other, as comparisons of their results reveal uncertainties that are not otherwise evident.

Implications:?Organic markers can be measured on currently acquired PM_2.5 filter samples by thermal methods. These markers can complement element, ion, and carbon fraction measurements from long-term speciation networks. Applying the positive matrix factorization and effective variance solutions for the chemical mass balance equations provides useful information on the accuracy of the source contribution estimates. Nonpolar compounds need to be complemented with polar compounds to better apportion cooking and secondary organic aerosol contributors.     

Comparison between sample-species specific uncertainties and estimated uncertainties for the source apportionment of the speciation trends network data

In order to use the US Environmental Protection Agency's speciation trends networks (STN) data in source apportionment studies with positive matrix factorization (PMF), uncertainties for each of the measured data points are required. Since STN data were not accompanied by sample-species specific uncertainties (SSU) prior to July 2003, a comprehensive set of fractional uncertainties was estimated by Kim et al. [2005. Estimation of organic carbon blank values and error structures of the speciation trends network data for source apportionments. Journal of Air and Waste Management Association 55, 1190–1199]. The objective of this study is to compare the use of the estimated fractional uncertainties (EFU) for the source apportionment of PM_2.5 (particulate matter less than 2.5 μm in aerodynamic diameter) measured at the STN monitoring sites with the results obtained using SSU. Thus, the source apportionment of STN PM_2.5 data were performed and their contributions were estimated through the application of PMF for two selected STN sites, Elizabeth, NJ and Baltimore, MD with both SSU and EFU for the elements measured by X-ray fluorescence. The PMF resolved factor profiles and contributions using EFU were similar to those using SSU at both monitoring sites. The comparisons of normalized concentrations indicated that the STN SSU were not well estimated. This study supports the use of EFU for the STN samples to provide useful error structure for the source apportionment studies of the STN data.     

Determination of PM2.5 sources using time-resolved integrated source and receptor models

Multivariate statistical techniques are applied to particulate matter (PM) and meteorological data to identify the sources responsible for evening PM spikes at Sunland Park, NM (USA). The statistical techniques applied are principal components analysis (PCA), redundancy analysis (RDA), and absolute principal components scores analysis (APCSA), and the data evaluated are 3-h average (6–9 p.m.) PM_2.5 mass and chemical composition and 1-h average PM_2.5 and PM₁₀ mass and environmental data collected in the winter of 2002. Although the interpretation of the data was complicated by the presence of sources which are likely changing in time (e.g. brick kilns), the multivariate analyses indicate that the evening high PM_2.5 is associated with burning-activities occurring to the south of Sunland Park, and these emissions are characterized by elevated Sb, Cl⁻, and elemental carbon; 68% of the PM_2.5 mass can be attributed to this source. The PM₁₀ evening peaks, on the other hand, are mainly caused by resuspended dust generated by vehicular movements south of the site and transported by the local terrain-induced drainage flow.