南昌市大气PM2.5中多环芳烃的来源解析

在南昌市布设5个采样点,分别代表工业区、居住区、交通干线区、商业区以及郊区,于2007年7～8月进行大气PM2.5的采样.根据5个采样点测得的数据,通过因子分析法判断南昌市大气PM2.5中多环芳烃的主要来源,再利用多元线性回归法确定各主要来源对多环芳烃的贡献率.结果表明,南昌市多环芳烃的主要来源为车辆排放源、高温加热源、燃煤污染源,对多环芳烃的贡献率分别为37.9%、28.2%、22.0%.     

Source apportionment of PM2.5 and PM10 aerosols in Brisbane (Australia) by receptor modelling

Aerosol samples for PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters less than 2.5 and 10 μm, respectively) were collected from 1993 to 1995 at five sites in Brisbane, a subtropical coastal city in Australia. This paper investigates the contributions of emission sources to PM_2.5 and PM₁₀ aerosol mass in Brisbane. Source apportionment results derived from the chemical mass balance (CMB), target transformation factor analysis (TTFA) and multiple linear regression (MLR) methods agree well with each other. The contributions from emission sources exhibit large variations in particle size with temporal and spatial differences. On average, the major contributors of PM₁₀ aerosol mass in Brisbane include: soil/road side dusts (25% by mass), motor vehicle exhausts (13%, not including the secondary products), sea salt (12%), Ca-rich and Ti-rich compounds (11%, from cement works and mineral processing industries), biomass burning (7%), and elemental carbon and secondary products contribute to around 15% of the aerosol mass on average. The major sources of PM_2.5 aerosols at the Griffith University (GU) site (a suburban site surrounded by forest area) are: elemental carbon (24% by mass), secondary organics (21%), biomass burning (15%) and secondary sulphate (14%). Most of the secondary products are related to motor vehicle exhausts, so, although motor vehicle exhausts contribute directly to only 6% of the PM_2.5 aerosol mass, their total contribution (including their secondary products) could be substantial. This pattern of source contribution is similar to the results for Rozelle (Sydney) among the major Australian studies, and is less in contributions from industrial and motor vehicular exhausts than the other cities. An attempt was made to estimate the contribution of rural dust and road side dust. The results show that road side dusts could contribute more than half of the crustal matter. More than 80% of the contribution of vehicle exhausts arises from diesel-fuelled trucks/buses. Biomass burning, large contributions of crustal matter, and/or local contributing sources under calm weather conditions, are often the cause of the high PM₁₀ episodes at the GU site in Brisbane.     

Source apportionment of fine particulate matter (PM2.5) at a rural Ohio River Valley site

Twenty four-hour averaged concentrations of fine particulate matter were collected at Athens, OH between March 2004 and November 2005 in an effort to characterize the nature of PM_2.5 and apportion its sources. PM_2.5 samples were chemically analyzed and positive matrix factorization was applied to this speciation data to identify the probable sources. PMF arrived at a 7-factor model to most accurately apportion sources of the PM_2.5 observed at Athens. Conditional probability function (CPF) and potential source contribution function (PSCF) were applied to the identified sources to investigate the geographical location of these sources. Secondary sulfate source dominated the contributions with a total contribution of 62.6% with the primary and secondary organic source following second with 19.9%. Secondary nitrate contributed a total of 6.5% with the steel production source and Pb- and Zn-source coming in at 3.1% and 2.9%, respectively. Crustal and mobile sources were small contributors (2.5% each) of PM_2.5 to the Athens region. The secondary sulfate, secondary organic and nitrate portrayed a clear seasonal nature with the sulfate and secondary organic peaking in the warm months and the nitrate reaching a high in the cold months. The high percentage of secondary sulfate observed at a rural site like Athens suggests the involvement of regional transport mechanisms.     

Source apportionment of particulate matter at urban mixed site in Indonesia using PMF

A receptor model of positive matrix factorization (PMF) was used to identify the emission sources of fine and coarse particulates in Bandung, a city located at about 150 km south-east of Jakarta. Total of 367 samples were collected at urban mixed site, Tegalega area, in Bandung City during wet and dry season in the period of 2001–2007. The samples of fine and coarse particulate matter were collected simultaneously using dichotomous samplers and mini-volume samplers. The Samples from dichotomous Samplers were analyzed for black carbon and elements while samples from mini-volume samplers were analyzed for ions. The species analyzed in this study were Na, Mg, Al, Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Pb, Cl^?, NO₃^?, SO₄^2?, and NH₄⁺. The data were then analyzed using PMF to determine the source factors. Different numbers of source factors were found during dry and wet season. During dry season, the main source factors for fine particles were secondary aerosol (NH₄)₂SO₄, electroplating industry, vehicle emission, and biomass burning, while for coarse particles, the dominant source factors were electroplating industry, followed by aged sea salt, volcanic dust, soil dust, and lime dust. During the wet season, the main source factors for fine particulate matter were vehicle emission and secondary aerosol. Other sources detected were biomass burning, lime dust, soil and volcanic dust. While for coarse particulate matter, the main source factors were sulphate-rich industry, followed by lime dust, soil dust, industrial emission and construction dust.     

Source apportionment of PM10 and PM2.5 in five Chilean cities using factor analysis

Chile is a fast-growing country with important industrial activities near urban areas. In this study, the mass and elemental concentrations of PM10 and PM2.5 were measured in five major Chilean urban areas. Samples of particles with diameter less than 10 microm (PM10) and 2.5 microm (PM2.5) were collected in 1998 in Iquique (northern Chile), Valparaiso, Vi?a del Mar, Rancagua (central Chile), and Temuco (southern Chile). Both PM10 and PM2.5 annual mean concentrations (PM10: 56.9-77.6 microg/m3; PM2.5: 22.4-42.6 microg/m3) were significantly higher than the corresponding European Union (EU) and U.S. Environmental Protection Agency (EPA) air quality standards. Moreover, the 24-hr PM10 and PM2.5 U.S. standards were exceeded infrequently for some of the cities (Rancagua and Valparaiso). Elements ranging from Mg to Pb were detected in the aerosol samples using X-ray fluorescence (XRF). For each of the five cities, factor analysis (FA) was applied to identify and quantify the sources of PM10 and PM2.5. The agreement between calculated and measured mass and elemental concentrations was excellent in most of the cities. Both natural and anthropogenic sources were resolved for all five cities. Soil and sea were the most important contributors to coarse particles (PM10-PM2.5), whereas their contributions to PM2.5 were negligible. Emissions from Cu smelters and oil refineries (and/or diesel combustion) were identified as important sources of PM2.5, particularly in the industrial cities of Rancagua, Valparaiso, and Vi?a del Mar. Finally, motor vehicles and wood burning were significant sources of both PM2.5 and PM10 in most of the cities (wood burning was not identified in Iquique).     

Source apportionment of Phoenix PM2.5 aerosol with the Unmix receptor model

The multivariate receptor model Unmix has been used to analyze a 3-yr PM2.5 ambient aerosol data set collected in Phoenix, AZ, beginning in 1995. The analysis generated source profiles and overall average percentage source contribution estimates (SCEs) for five source categories:gasoline engines (33 +/- 4%), diesel engines (16 +/- 2%), secondary SO4(2-) (19 +/- 2%), crustal/soil (22 +/- 2%), and vegetative burning (10 +/- 2%). The Unmix analysis was supplemented with scanning electron microscopy (SEM) of a limited number of filter samples for information on possible additional low-strength sources. Except for the diesel engine source category, the Unmix SCEs were generally consistent with an earlier multivariate receptor analysis of essentially the same data using the Positive Matrix Factorization (PMF) model. This article provides the first demonstration for an urban area of the capability of the Unmix receptor model.     

Source apportionment of airborne fine particulate matter in an underground mine

The chemical mass balance source apportionment technique was applied to an underground gold mine to assess the contribution of diesel exhaust, rock dust, oil mists, and cigarette smoke to airborne fine (<2.5 microm) particulate matter (PM). Apportionments were conducted in two locations in the mine, one near the mining operations and one near the exit of the mine where the ventilated mine air was exhausted. Results showed that diesel exhaust contributed 78-98% of the fine particulate mass and greater than 90% of the fine particle carbon, with rock dust making up the remainder. Oil mists and cigarette smoke contributions were below detection limits for this study. The diesel exhaust fraction of the total fine PM was higher than the recently implemented mine air quality standards based on total carbon at both sample locations in the mine.     

Source apportionment of airborne particulate matter using organic compounds as tracers

A chemical mass balance receptor model based on organic compounds has been developed that relates source contributions to airborne fine particle mass concentrations. Source contributions to the concentrations of specific organic compounds are revealed as well. The model is applied to four air quality monitoring sites in southern California using atmospheric organic compound concentration data and source test data collected specifically for the purpose of testing this model. The contributions of up to nine primary particle source types can be separately identified in ambient samples based on this method, and approximately 85% of the organic fine aerosol is assigned to primary sources on an annual average basis. The model provides information on source contributions to fine mass concentrations, fine organic aerosol concentrations and individual organic compound concentrations. The largest primary source contributors to fine particle mass concentrations in Los Angeles are found to include diesel engine exhaust, paved road dust, gasoline-powered vehicle exhaust, plus emissions from food cooking and wood smoke, with smaller contribution from tire dust, plant fragments, natural gas combustion aerosol, and cigarette smoke. Once these primary aerosol source contributions are added to the secondary sulfates, nitrates and organics present, virtually all of the annual average fine particle mass at Los Angeles area monitoring sites can be assigned to its source.     

Source apportionment of fine particulate matter in Phoenix, AZ, using positive matrix factorization

Speciated particulate matter (PM)2.5 data collected as part of the Interagency Monitoring of Protected Visual Environments (IMPROVE) program in Phoenix, AZ, from April 2001 through October 2003 were analyzed using the multivariate receptor model, positive matrix factorization (PMF). Over 250 samples and 24 species were used, including the organic carbon and elemental carbon analytical temperature fractions from the thermal optical reflectance method. A two-step approach was used. First, the species excluding the carbon fractions were used, and initially eight factors were identified; non-soil potassium was calculated and included to better refine the burning factor. Next, the mass associated with the burning factor was removed, and the data set rerun with the carbon fractions. Results were very similar (i.e., within a few percent), but this step enabled a separation of the mobile factor into gasoline and diesel vehicle emissions. The identified factors were burning (on average 2% of the mass), secondary transport (7%), regional power generation (13%), dust (25%), nitrate (9%), industrial As/Pb/Se (2%), Cu/Ni/V (7%), diesel (9%), and general mobile (26%). The overall contribution from mobile sources also increased, as some mass (OC and nitrate) from the nitrate and regional power generation factors were apportioned with the mobile factors. This approach allowed better apportionment of carbon as well as total mass. Additionally, the use of multiple supporting analyses, including air mass trajectories, activity trends, and emission inventory information, helped increase confidence in factor identification.     

PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia, Spain

Levels of total suspended particles, PM10, PM2.5 and PM1 were continuously monitored at an urban kerbside in the Metropolitan area of Barcelona from June 1999 to June 2000. The results show that hourly levels of PM2.5 and PM1 are consistent with the daily cycle of gaseous pollutants emitted by traffic, whereas TSP and PM10 do not follow the same trend, at least in the diurnal period. The PM2.5/PM10 ratio is dependent on the traffic emissions, whereas additional contribution sources for the >10 μm fraction must be taken into account in the diurnal period. Different PM10 and PM2.5 source apportionment techniques were compared. A methodology based on the chemical determination of 83% of both PM10 and PM2.5 masses allowed us to quantify the marine (4% in PM10 and <1% in PM2.5), crustal (26% in PM10 and 8% in PM2.5) and anthropogenic (54% in PM10 and 73% in PM2.5) loads. Peaks of crustal contribution to PM10 (up to 44% of the PM10 mass) were recorded under Saharan air mass intrusions. A different seasonal trend was observed for levels of sulphate and nitrate, probably as a consequence of the different thermodynamic behaviour of these PM species and the higher summer oxidation rate of SO₂.     

Source apportionment of ambient total suspended particulates and coarse particulate matter in urban areas of Jiaozuo, China

Approximately 750 total suspended particulates (TSPs) and coarse particulate matter (PM10) filter samples from six urban sites and a background site and >210 source samples were collected in Jiaozuo City during January 2002 to April 2003. They were analyzed for mass and abundances of 25 chemical components. Seven contributive sources were identified, and their contributions to ambient TSP/PM10 levels at the seven sites in three seasons (spring, summer, and winter days) and a "whole" year were estimated by a chemical mass balance (CMB) receptor model. The spatial TSP average was high in spring and winter days at a level of approximately 530 microg/m(3) and low in summer days at 456 microg/m(3); however, the spatial PMo0 average exhibited little variation at a level of approximately 325 microg/m(3), and PM10-to-TSP ratios ranged from 0.58 to 0.81, which suggested heavy particulate matter pollution existing in the urban areas. Apportionment results indicated that geological material was the largest contributor to ambient TSP/PM10 concentrations, followed by dust emissions from construction activities, coal combustion, secondary aerosols, vehicle movement, and other industrial sources. In addition, paved road dust and re-entrained dust were also apportioned to the seven source types and found soil, coal combustion, and construction dust to be the major contributors.     

Characterisation of PM10 and PM2.5 particulate matter in the ambient air of Milan (Italy)

24-h simultaneous samplings of PM10 and PM2.5 particulate matter (PM) have been carried out during the period December 1997–September 1998 in the central urban area of Milan. The mass concentrations of the two fractions showed significant daily variations linked to different thermodynamic conditions of the planetary boundary layer (PBL) and characterised by higher values during wintertime. The elemental composition, determined by energy dispersive X-ray fluorescence technique, was quite different in the two fractions: in the finer one the presence of elements with crustal origin is reduced while the anthropogenic elements, with a relevant environmental and health impact, appear to be enriched. The composition data allowed a quantification of two major components of the atmospheric particulate: sulphates (mainly of secondary origin) and particles with crustal origin. An important but unmeasured component is likely constituted by organic and elemental carbon compounds.The multivariate analysis of elements, gaseous pollutants and mass concentration data-sets leads to the identification of four main sources contributing to PM10 and PM2.5 composition: vehicles exhaust emissions, resuspended crustal dust, secondary sulphates and industrial emissions. The existence of a possible background component with non-local origin is also suggested.     

南宁市大气颗粒物TSP、PM10、PM2.5污染水平研究

2002年在南宁市的5个典型城市功能区内,共采集了125个大气样品(按季节分别采集),初步调查了大气中颗粒物TSP、PM10、PM2.5的污染状况。结果表明,南宁市TSP、PM10、PM2.5的污染很严重,超标率分别为67．5％、82．5％、92．5％,对人体健康危害更大的PM2．5占到了PM10的63．5％左右。重污染区PM2.5的浓度超过轻污染区近一倍。     

Source contributions to fine particulate matter in an urban atmosphere

This paper proposes a practical method for estimating source attribution by using a three-step methodology. The main objective of this study is to explore the use of the three-step methodology for quantifying the source impacts of 24-h PM2.5 particles at an urban site in Seoul, Korea. 12-h PM2.5 samples were collected and analyzed for their elemental composition by ICP-AES/ICP-MS/AAS to generate the source composition profiles. In order to assess the daily average PM2.5 source impacts, 24-h PM2.5 and polycyclic aromatic hydrocarbons (PAH) ambient samples were simultaneously collected at the same site. The PM2.5 particle samples were then analyzed for trace elements. Ionic and carbonaceous species concentrations were measured by ICP-AES/ICP-MS/AAS, IC, and a selective thermal MnO2 oxidation method. The 12-h PM2.5 chemical data was used to estimate possible source signatures using the principal component analysis (PCA) and the absolute principal component scores method followed by the multiple linear regression analysis. The 24-h PM2.5 source categories were extracted with a combination of PM2.5 and some PAH chemical data using the PCA, and their quantitative source contributions were estimated by chemical mass balance (CMB) receptor model using the estimated source profiles and those in the literature. The results of PM2.5 source apportionment using the 12-h derived source composition profiles show that the CMB performance indices; chi2, R2, and percent of mass accounted for are 2.3%, 0.97%, and 100.7%, which are within the target range specified. According to the average PM2.5 source contribution estimate results, motor vehicle exhaust was the major contributor at the sampling site, contributing 26% on average of measured PM2.5 mass (41.8 microg m-3), followed by secondary sulfate (23%) and nitrate (16%), refuse incineration (15%), soil dust (13%), field burning (4%), oil combustion (2.7%), and marine aerosol (1.3%). It can be concluded that quantitative source attribution to PM2.5 in an urban area where source profiles have not been developed can be estimated using the proposed three-step methodology approach.     

Source apportionment of indoor, outdoor, and personal PM2.5 in Seattle, Washington, using positive matrix factorization

As part of a large exposure assessment and health-effects panel study, 33 trace elements and light-absorbing carbon were measured on 24-hr fixed-site filter samples for particulate matter with an aerodynamic diameter <2.5 microm (PM2.5) collected between September 26, 2000, and May 25, 2001, at a central outdoor site, immediately outside each subject's residence, inside each residence, and on each subject (personal sample). Both two-way (PMF2) and three-way (PMF3) positive matrix factorization were used to deduce the sources contributing to PM2.5. Five sources contributing to the indoor and outdoor samples were identified: vegetative burning, mobile emissions, secondary sulfate, a source rich in chlorine, and a source of crustal-derived material. Vegetative burning contributed more PM2.5 mass on average than any other source in all microenvironments, with average values estimated by PMF2 and PMF3, respectively, of 7.6 and 8.7 microg/m3 for the outdoor samples, 4 and 5.3 microg/m3 for the indoor samples, and 3.8 and 3.4 microg/m3 for the personal samples. Personal exposure to the combustion-related particles was correlated with outdoor sources, whereas exposure to the crustal and chlorine-rich particles was not. Personal exposures to crustal sources were strongly associated with personal activities, especially time spent at school among the child subjects.     

Source apportionment of particulate matter in a large city of southeastern Po Valley (Bologna, Italy)

This study reports the results of an experimental research project carried out in Bologna, a midsize town in central Po valley, with the aim at characterizing local aerosol chemistry and tracking the main source emissions of airborne particulate matter. Chemical speciation based upon ions, trace elements, and carbonaceous matter is discussed on the basis of seasonal variation and enrichment factors. For the first time, source apportionment was achieved at this location using two widely used receptor models (principal component analysis/multi-linear regression analysis (PCA/MLRA) and positive matrix factorization (PMF)). Four main aerosol sources were identified by PCA/MLRA and interpreted as: resuspended particulate and a pseudo-marine factor (winter street management), both related to the coarse fraction, plus mixed combustions and secondary aerosol largely associated to traffic and long-lived species typical of the fine fraction. The PMF model resolved six main aerosol sources, interpreted as: mineral dust, road dust, traffic, secondary aerosol, biomass burning and again a pseudo-marine factor. Source apportionment results from both models are in good agreement providing a 30 and a 33 % by weight respectively for PCA-MLRA and PMF for the coarse fraction and 70 % (PCA-MLRA) and 67 % (PMF) for the fine fraction. The episodic influence of Saharan dust transport on PM₁₀ exceedances in Bologna was identified and discussed in term of meteorological framework, composition, and quantitative contribution.     

Source apportionment of fine particulate matter in the southeastern United States

Particulate matter (PM) less than 2.5 microm in size (PM2.5) source apportionment by chemical mass balance receptor modeling was performed to enhance regional characterization of source impacts in the southeastern United States. Secondary particles, such as NH4HSO4, (NH4)2SO4, NH4NO3, and secondary organic carbon (OC) (SOC), formed by atmospheric photochemical reactions, contribute the majority (>50%) of ambient PM2.5 with strong seasonality. Source apportionment results indicate that motor vehicle and biomass burning are the two main primary sources in the southeast, showing relatively more motor vehicle source impacts rather than biomass burning source impacts in populated urban areas and vice versa in less urbanized areas. Spatial distributions of primary source impacts show that each primary source has distinctively different spatial source impacts. Results also find impacts from shipping activities along the coast. Spatiotemporal correlations indicate that secondary particles are more regionally distributed, as are biomass burning and dust, whereas impacts of other primary sources are more local.     

Arsenic speciation of atmospheric particulate matter (PM10) in an industrialised urban site in southwestern Spain

An arsenic speciation study has been performed in PM10 samples collected on a fortnight basis in the city of Huelva (SW Spain) during 2001 and 2002. The arsenic species were extracted from the PM10 filters using a NH2OH x HCl solution and sonication, and determined by HPLC-HG-AFS. The mean bulk As concentration of the samples analyzed during 2001 and 2002 slightly exceed the mean annual 6 ng m(-3) target value proposed by the European Commission for 2013, arsenate [As(V)] being responsible for the high level of arsenic. The speciation analyses showed that As(V) was the main arsenic species found, followed by arsenite [As(III)] (mean 6.5 and 7.8 ng m(-3) for As(V), mean 1.2 and 2.1 ng m(-3) for As(III), in 2001 and 2002, respectively). The high levels of arsenic species found in PM10 in Huelva have a predominant industrial origin, such as the one from a nearby copper smelter, and do not present a seasonal pattern. The highest daily levels of arsenic species correspond to synoptic conditions in which the winds with S and SW components transport the contaminants from the main emission source. The frequent African dust outbreaks over Huelva may result in an increment of mass levels of PM10, but do not represent a significant input of arsenic in comparison to the anthropogenic source. The rural background levels of arsenic around Huelva are rather high, in comparison to other rural or urban areas in Spain, showing a relatively high atmosphere residence time of arsenic. This work shows the importance of arsenic speciation in studies of aerosol chemistry, due to the presence of arsenic species [As(III) and As(V)] with distinct toxicity.     

南昌市夏季PM_(2.5)中多环芳烃来源解析

在南昌市设立了5个不同功能区采样点,分别为居民区、工业区、商业区、交通干线区以及郊区,于2008年夏季进行PM2.5采样,对样品进行测定和分析后,通过因子分析法判断PM2.5中多环芳烃(PAHs)的主要污染源,再利用多元线性回归法确定各主要污染源对PAHs的贡献率。结果表明,南昌市夏季PM2.5中PAHs的主要污染源为车辆排放源、高温加热源、燃煤污染源,它们对PAHs的贡献率分别为37.9%、28.2%和22.0%;要控制南昌市夏季PM2.5中的PAHs,主要是要对机动车尾气排放量进行控制,并加强机动车尾气治理工作。     

Chemical composition and source apportionment of PM2.5 particles in the Sihwa area, Korea

To investigate the chemical characteristics of fine particles in the Sihwa area, Korea, atmospheric aerosol samples were collected using a dichotomous PM10 sampler and two URG PM2.5 cyclone samplers during five intensive sampling periods between February 1998 and February 1999. The Inductively Coupled Plasma (ICP)-Atomic Emission Spectrometry (AES)/ICP-Mass Spectrometry (MS), ion chromatograph (IC), and thermal manganese dioxide oxidation (TMO) methods were used to analyze the trace elements, ionic species, and carbonaceous species, respectively. Backward trajectory analysis, factor analysis, and a chemical mass balance (CMB) model were used to estimate quantitatively source contributions to PM2.5 particles collected in the Sihwa area. The results of PM2.5 source apportionment using the CMB7 receptor model showed that (NH4)2SO4 was, on average, the major contributor to PM2.5 particles, followed by nontraffic organic carbon (OC) emission, NH4NO3, agricultural waste burning, motor vehicle emission, road dust, waste incineration, marine aerosol, and others. Here, the nontraffic OC sources include primary anthropogenic OC emitted from the industrial complex zone, secondary OC, and organic species from distant sources. The source impact of waste incineration emission became significant when the dominant wind directions were from southwest and west sectors during the sampling periods. It was found that PM2.5 particles in the Sihwa area were influenced mainly by both anthropogenic local sources and long-range transport and transformation of air pollutants.