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.     

Comparison of multi-receptor and single-receptor trajectory source apportionment (TSA) methods 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 residence time or conditional probability method developed by Ashbaugh et al. [1985. A residence time probability analysis of sulfur concentrations at Grand Canyon National Park. Atmospheric Environment 19(8), 1263–1270] uses a single receptor at a time, whereas the new multi-receptor (MURA) method developed here uses several receptors at once in an attempt to detect the sources with higher accuracy. The methods were first tested using a simulation with a single source and then with another simulation using four sources. The ability of the methods to detect the sources was quantified for each simulation. The MURA trajectory method proved to be superior at identifying sources for these simulations.     

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,...     

Feasibility of soil dust source apportionment by the pyrolysis-gas chromatography/mass spectrometry method

This study tested the feasibility of using pyrolysis (Py)-gas chromatography (GC)/mass spectrometry (MS) to obtain organic chemical species data suitable for source apportionment modeling of soil-derived coarse particulate matter (PM10) dust on ambient filters. A laboratory resuspension apparatus was used with known soils to generate simulated receptor filter samples loaded with approximately 0.4 mg of PM10 dust, which is within the range of mass loading on ambient filters. Py-GC/MS at 740 degrees C generated five times more resolvable compounds than were obtained with thermal desorption GC/MS at 315 degrees C. The identified compounds were consistent with literature from Py experiments using larger samples of bulk soils. A subset of 91 organic species out of the 178 identified Py products was used as input to CMB8 software in a demonstration of source apportionment using laboratory-generated mixtures simulating ambient filter samples. The 178 quantified organic species obtained by Py of soil samples is an improvement compared with the 38 organic species obtained by thermal desorption of soils and the four functionally defined organic fractions reported by thermal/ optical reflectance. Significant differences in the concentration of specific species were seen between samples from different sites, both geographically distant and close, using analysis of variance and cluster analysis. This feasibility study showed that Py-GC/MS can generate useful source profile data for receptor modeling and justifies continued method development.     

大气颗粒物源解析技术研究进展

大气颗粒物源解析技术在环境管理中发挥着越来越重要的作用。本文从受体模型、样品的处理技术、源解析技术发展的最新趋势三方面介绍了大气颗粒物源解析技术的研究进展。     

水环境多环芳烃源解析研究进展

城市化和工业化给环境带来的潜在危害引起人们对环境质量的重视。污染物来源解析研究的成果为环境管理提供了有效的工具。在查阅大量文献的基础上,综述了应用于水环境尤其是沉积物中多环芳烃源解析的主要理论方法和应用模型,并初步提出了沉积物中多环芳烃源解析方法。     

水环境多环芳烃源解析研究进展

城市化和工业化给环境带来的潜在危害引起人们对环境质量的重视.污染物来源解析研究的成果为环境管理提供了有效的工具.在查阋大量文献的基础上,综述了应用于水环境尤其是沉积物中多环芳烃源解析的主要理论方法和应用模型,并初步提出了沉积物中多环芳烃源解析方法.     

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.     

杭州市主城区大气颗粒物的酸缓冲能力及来源解析

通过酸缓冲能力的测定实验,研究杭州市主城区大气颗粒物的酸缓冲能力,并利用二重源解析技术,解析了大气颗粒物中碱性组分的来源.结果表明,杭州市主城区大气颗粒物呈弱碱性,对降水酸度有一定的缓冲作用,但作用较小.总体而言,TSP与PM10的酸缓冲能力与其浓度呈负相关,但相关关系不明显,TSP的酸缓冲能力比PM10强.Ca是影响大气颗粒物酸缓冲能力的关键化学组分.TSP的酸缓冲能力主要来自建筑水泥尘.     

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.     

The Steubenville Comprehensive Air Monitoring Program (SCAMP): concentrations and solubilities of PM(2.5) trace elements and their implications for source apportionment and health research

The elemental compositions of the water-soluble and acid-digestible fractions of 24-hr integrated fine particulate matter (PM(2.5)) samples collected in Steubenville, OH, from 2000 to 2002 were determined using dynamic reaction cell inductively coupled plasma-mass spectrometry. The water-soluble elemental compositions of PM(2.5) samples collected at four satellite monitoring sites in the surrounding region were also determined. Fe was the most abundant but least water soluble of the elements determined at the Steubenville site, having a mean ambient concentration of 272 ng/m3 and a median fractional solubility of 6%. Fe solubility and its correlations with SO4(2-) and temperature varied significantly by season, consistent with the hypothesis that secondary sulfates may help to mobilize soluble Fe under suitable summertime photochemical conditions. Significantly higher ambient concentrations were observed at Steubenville than at each of the four satellite sites for 10 of the 18 elements (Al, As, Ca, Cd, Fe, Mg, Mn, Na, Pb, and Zn) determined in the water-soluble PM(2.5) fraction. Concentrations of Fe, Mn, and Zn at Steubenville were substantially higher than concentrations reported recently for larger U.S. cities. Receptor modeling identified seven sources affecting the Steubenville site. An (NH4)2SO4-dominated source, likely representing secondary PM(2.5) from coal-fired plants to the west and southwest of Steubenville, accounted for 42% of the PM(2.5) mass, and two sources likely dominated by emissions from motor vehicles and from iron and steel facilities in the immediate Steubenville vicinity accounted for 20% and 10%, respectively. Other sources included an NH4NO3 source (15%), a crustal source (6%), a mixed nonferrous metals and industrial source (3%), and a primary coal combustion source (3%). Results suggest the importance of very different regional and local source mechanisms in contributing to PM(2.5) mass at Steubenville and reinforce the need for further research to elucidate whether metals such as Fe, Mn, and Zn play a role in the PM(2.5) health effects observed previously there.     

Influence of organic and inorganic markers in the source apportionment of airborne PM10 in Zaragoza (Spain) by two receptor models

Improving knowledge on the apportionment of airborne particulate matter will be useful to handle and fulfill the legislation regarding this pollutant. The main aim of this work was to assess the influence of markers in the source apportionment of airborne PM10, in particular, whether the use of particle polycyclic aromatic hydrocarbon (PAH) and ions provided similar results to the ones obtained using not only the mentioned markers but also gas phase PAH and trace elements. In order to reach this aim, two receptor models: UNMIX and positive matrix factorization were applied to two sets of data in Zaragoza city from airborne PM10, a previously reported campaign (2003–2004) (Callén et al. Chemosphere 76:1120-1129, 2009), where PAH associated to the gas and particle phases, ions and trace elements were used as markers and a long sampling campaign (2001–2009), where only PAH in the particle phase and ions were analyzed. For both campaigns, positive matrix factorization was able to explain a higher number of sources than the UNMIX model. Independently of the sampling campaign and the receptor model used, soil resuspension was the main PM10 source, especially in the warm period (21st March–21st September), where most of the PM10 exceedances were produced. Despite some of the markers of anthropogenic sources were different for both campaigns, common sources associated to different combustion sources (coal, light-oil, heavier-oil, biomass, and traffic) were found and PAH in particle phase and ions seemed to be good markers for the airborne PM10 apportionment.     

Spatial distribution and source apportionment of water pollution in different administrative zones of Wen-Rui-Tang (WRT) river watershed, China

Water quality degradation in river systems has caused great concerns all over the world. Identifying the spatial distribution and sources of water pollutants is the very first step for efficient water quality management. A set of water samples collected bimonthly at 12 monitoring sites in 2009 and 2010 were analyzed to determine the spatial distribution of critical parameters and to apportion the sources of pollutants in Wen-Rui-Tang (WRT) river watershed, near the East China Sea. The 12 monitoring sites were divided into three administrative zones of urban, suburban, and rural zones considering differences in land use and population density. Multivariate statistical methods [one-way analysis of variance, principal component analysis (PCA), and absolute principal component score—multiple linear regression (APCS-MLR) methods] were used to investigate the spatial distribution of water quality and to apportion the pollution sources. Results showed that most water quality parameters had no significant difference between the urban and suburban zones, whereas these two zones showed worse water quality than the rural zone. Based on PCA and APCS-MLR analysis, urban domestic sewage and commercial/service pollution, suburban domestic sewage along with fluorine point source pollution, and agricultural nonpoint source pollution with rural domestic sewage pollution were identified to the main pollution sources in urban, suburban, and rural zones, respectively. Understanding the water pollution characteristics of different administrative zones could put insights into effective water management policy-making especially in the area across various administrative zones.     

浙东沿海城市大气颗粒物污染特征及来源解析研究

对2009年夏季浙东沿海地区环境空气质量进行监测,监测大气颗粒物(TSP、PM10、PM2.5、PM1.0)浓度,分析颗粒物污染特征、水溶性离子及无机元素组成,运用化学质量平衡受体模型(CMB模型)对浙东沿海地区大气TSP来源进行解析.结果表明,浙东沿海地区的大气颗粒物主要以细颗粒物为主,颗粒物中主要的水溶性离子为SO2-4、NH+4、Ca2+,土壤尘是该地区大气TSP的主要来源,北仑、乐清和奉化TSP中土壤尘的分担率分别达到55.49%、42.52%、40.70%,各监测点TSP来源具有一定的地域特征.     

Seasonal variability and source apportionment of metals in the atmospheric deposition in Belgrade

The primary objective of this study is to assess anthropogenic impacts on the environment by determination of element atmospheric depositions. Bulk depositions were collected monthly, from June 2002 to December 2006, at three urban locations in Belgrade. Concentrations of Al, V, Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb were analyzed by atomic absorption spectrometry and the current deposition fluxes of atmospheric metals were established. Fourier analysis was applied in order to investigate seasonal variation of the monthly data set. Nickel, V, Fe and Al showed pronounced seasonal dependence, while seasonal variation of the other elements was not evident. The enrichment factors of Pb, Zn, Cd and Cu were obviously above those who could have been caused by natural processes, indicating a mainly anthropogenic origin. Nickel was intermediately enriched suggesting participation of both natural and anthropogenic sources. The multivariate receptor model, Unmix, was used to analyze a 5-yr element atmospheric depositions data set. Three main source profiles (mixed road dust, oil combustion and metal processing) were identified and the overall average percentage source contributions determined.     

Positive matrix factorization as source apportionment of soil lead and cadmium around a battery plant (Changxing County,China)

Chemical compositions of soil samples are multivariate in nature and provide datasets suitable for the application of multivariate factor analytical techniques. One of the analytical techniques, the positive matrix factorization (PMF), uses a weighted least square by fitting the data matrix to determine the weights of the sources based on the error estimates of each data point. In this research, PMF was employed to apportion the sources of heavy metals in 104 soil samples taken within a 1-km radius of a lead battery plant contaminated site in Changxing County, Zhejiang Province, China. The site is heavily contaminated with high concentrations of lead (Pb) and cadmium (Cd). PMF successfully partitioned the variances into sources related to soil background, agronomic practices, and the lead battery plants combined with a geostatistical approach. It was estimated that the lead battery plants and the agronomic practices contributed 55.37 and 29.28 %, respectively, for soil Pb of the total source. Soil Cd mainly came from the lead battery plants (65.92 %), followed by the agronomic practices (21.65 %), and soil parent materials (12.43 %). This research indicates that PMF combined with geostatistics is a useful tool for source identification and apportionment.     

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.     

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.