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.     

One-year study of the elemental composition and source apportionment of PM10 aerosols in Florence, Italy

An extensive investigation was carried out for the characterisation of the air particulate composition in Florence. The aim was to determine the aerosol elemental concentrations, as well as to identify pollution sources. For our investigation, the external Particle-Induced X-Ray Emission-Particle-Induced gamma-Ray Emission beam facility of the Istituto Nazionale di Fisica Nucleare, Van de Graaff accelerator at the Physics Department of the Florence University was used. We report the results of the analysis of a long temporal series (approximately 1 yr) of PM10 particulate samples, collected on Millipore filters on a daily basis in three different sites (characterised by different urban settings). Daily concentrations of more than 20 elements were detected. The long sampling period (approximately 1 yr) allowed a comparison with the air quality recommended values and the identification of seasonal variations. Four main sources (traffic, oil-combustion, soil-dust, and wind transported sea-salt) were extracted with the help of Principal Component Analysis (PCA). An absolute PCA showed traffic to be the major source both in the high traffic site and in the urban background site.     

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 PM10 in six cities of northern China

Ambient PM₁₀ was sampled in six northern China cities (Urumqi, Yinchuan, Taiyuan, Anyang, Tianjin and Jinan) from December 1999 to July 2002, and analyzed for 16 chemical elements, two water-soluble ions, total carbon, and organic carbon. In addition, chemical source profiles consisting of the same particulate components were obtained from a number of naturally occurring geological sources (soil dust from exposed lands) and sources of atmospheric particulates resulting from human activities (resuspended dust, cement, coal combustion fly ash, vehicle exhaust, and secondary particles). Ambient and source data were used in a chemical mass balance (CMB) receptor model to determine the major source of PM₁₀ in these six cities. Results of CMB modeling showed that the major source of ambient PM₁₀ in all the cities was resuspended dust. Significant contributions from coal fly ash were also found in all six cities.     

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).     

Estimation of source apportionment and potential source locations of PM2.5 at a west coastal IMPROVE site

Particle composition data for PM_2.5 samples collected at Kalmiopsis Interagency Monitoring of Protected Visual Environments (IMPROVE) site in southwestern Oregon from March 2000 to May 2004 were analyzed to provide source identification and apportionment. A total of 493 samples were collected and 32 species were analyzed by particle induced X-ray emission, proton elastic scattering analysis, photon-induced X-ray fluorescence, ion chromatography, and thermal optical reflectance methods. Positive matrix factorization (PMF) was used to estimate the source profiles and their mass contributions. The PMF modeling identified nine sources. In the Kalmiopsis site, the average mass was apportioned to wood/field burning (38.4%), secondary sulfate (26.9%), airborne soil including Asian dust (8.6 %), secondary nitrate (7.6%), fresh sea salt (5.8%), OP-rich sulfate (4.9%), aged sea salt (4.5 %), gasoline vehicle (1.9%), and diesel emission (1.4%). The potential source contribution function (PSCF) was then used to help identify likely locations of the regional sources of pollution. The PSCF map for wood/field burning indicates there is a major potential source area in the Siskiyou County and eastern Oregon. The potential source locations for secondary sulfate are found in western Washington, northwestern Oregon, and the near shore Pacific Ocean where there are extensive shipping lanes. It was not possible to extract a profile directly attributable to ship emissions, but indications of their influence are seen in the secondary sulfate and aged sea salt compositions.     

南京大气细粒子中重金属污染特征及来源解析

利用2011年1月、4月、7月和10月在南京市区和北郊采集的气溶胶样品,研究了南京大气细粒子中zn、Ph、Hg、As和cd5种重金属的污染水平,通过元素相关性分析和因子分析方法,对细粒子中这些重金属的污染来源进行了初步解析。结果表明,南京大气细粒子及其重金属污染严重,北郊普遍比市区严重;As严重超标,cd在南京北郊超标约5倍,zn在市区与北郊的质量浓度均高于其他重金属元素。每种重金属的浓度均随季节而变化。市区细粒子中,As和zn可能主要与燃煤、轮胎灰尘和建筑扬尘等有关,Pb、Hg和cd主要来自交通尘、城市垃圾焚烧等。北郊细粒子中,As、Hg和zn主要来源于燃煤、钢铁冶炼等工业,Pb和cd主要与农作物秸秆燃烧、汽车尾气、道路扬尘等影响有关。     

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.     

Concentrations and source apportionment of PM10 and associated elemental and ionic species in a lignite-burning power generation area of southern Greece

Ambient concentrations of PM₁₀ and associated elemental and ionic species were measured over the cold and the warm months of 2010 at an urban and two rural sites located in the lignite-fired power generation area of Megalopolis in Peloponnese, southern Greece. The PM₁₀ concentrations at the urban site (44.2?±?33.6 μg m^?3) were significantly higher than those at the rural sites (23.7?±?20.4 and 22.7?±?26.9 μg m^?3). Source apportionment of PM₁₀ and associated components was accomplished by an advanced computational procedure, the robotic chemical mass balance model (RCMB), using chemical profiles for a variety of local fugitive dust sources (power plant fly ash, flue gas desulfurization wet ash, feeding lignite, infertile material from the opencast mines, paved and unpaved road dusts, soil), which were resuspended and sampled through a PM₁₀ inlet onto filters and then chemically analyzed, as well as of other common sources such as vehicular traffic, residential oil combustion, biomass burning, uncontrolled waste burning, marine aerosol, and secondary aerosol formation. Geological dusts (road/soil dust) were found to be major PM₁₀ contributors in both the cold and warm periods of the year, with average annual contribution of 32.6 % at the urban site vs. 22.0 and 29.0 % at the rural sites. Secondary aerosol also appeared to be a significant source, contributing 22.1 % at the urban site in comparison to 30.6 and 28.7 % at the rural sites. At all sites, the contribution of biomass burning was most significant in winter (28.2 % at the urban site vs. 14.6 and 24.6 % at the rural sites), whereas vehicular exhaust contribution appeared to be important mostly in the summer (21.9 % at the urban site vs. 11.5 and 10.5 % at the rural sites). The highest contribution of fly ash (33.2 %) was found at the rural site located to the north of the power plants during wintertime, when winds are favorable. In the warm period, the highest contribution of fly ash was found at the rural site located to the south of the power plants, although it was less important (7.2 %). Moderate contributions of fly ash were found at the urban site (5.4 and 2.7 % in the cold and the warm period, respectively). Finally, the mine field was identified as a minor PM₁₀ source, occasionally contributing with lignite dust and/or deposited wet ash dust under dry summer conditions, with the summertime contributions ranging between 3.1 and 11.0 % among the three sites. The non-parametric bootstrapped potential source contribution function analysis was further applied to localize the regions of sources apportioned by the RCMB. For the majority of sources, source regions appeared as being located within short distances from the sampling sites (within the Peloponnesse Peninsula). More distant Greek areas of the NNE sector also appeared to be source regions for traffic emissions and secondary calcium sulfate dust.     

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.     

Source apportionment of visual impairment during the California regional PM10/PM2.5 air quality study

Gases and particulate matter predictions from the UCD/CIT air quality model were used in a visibility model to predict source contributions to visual impairment in the San Joaquin Valley (SJV), the southern portion of California's Central Valley, during December 2000 and January 2001. Within the SJV, daytime (0800–1700 PST) light extinction was dominated by scattering associated with airborne particles. Measured daytime particle scattering coefficients were compared to predicted values at approximately 40 locations across the SJV after correction for the increased temperature and decreased relative humidity produced by “smart heaters” placed upstream of nephelometers. Mean fractional bias and mean fractional error were ?0.22 and 0.65, respectively, indicating reasonable agreement between model predictions and measurements. Particulate water, nitrate, organic matter, and ammonium were the major particulate species contributing to light scattering in the SJV. Daytime light extinction in the SJV averaged between December 25, 2000 and January 7, 2001 was mainly associated with animal ammonia sources (28%), diesel engines (18%), catalyst gasoline engines (9%), other anthropogenic sources (9%), and wood smoke (7%) with initial and boundary conditions accounting for 13%. The source apportionment results from this study apply to wintertime conditions when airborne particulate matter concentrations are typically at their annual maximum. Further study would be required to quantify source contributions to light extinction in other seasons.     

Seasonal variation of chemical composition and source apportionment of PM2.5 in Pune,India

Environmental Science and Pollution Research - Particulate matter with size less than or equal to 2.5&nbsp;μm (PM2.5) samples were collected from an urban site Pune, India, during April...     

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.     

Chemical fingerprint and source apportionment of PM 2.5 in highly polluted events of southern Taiwan

To investigate the spatial distribution and diurnal variation of the chemical composition of PM2.5 pollution in an industrial city of southern Taiwan, 12-h PM2.5 was diurnally continuously collected simultaneously at the Kaoping Air Quality Zone (KAQZ) during one highly PM2.5-polluted episode. Water-soluble ions, metallic elements, carbonaceous contents, dicarboxylic acids, and anhydrosugars were analyzed to characterize the chemical fingerprint of PM2.5. Backward trajectory simulation and chemical mass balance (CMB) receptor modeling were applied to identify the potential sources of PM2.5 and their contributions. It showed that Chaozhou (rural area) accompanying the highest SORs and NORs suffered from the most severe PM2.5 pollution during the episode. Sulfate (SO42−) was probably formed by the atmospheric chemical reaction in the daytime, while NO3− processed at nighttime at the KAQZ. A homogeneous formation of NO3− occurred at Chaozhou. The concentrations of Zn, Pb, Fe, Cu, V, and Al, mainly emitted from anthropogenic sources, increased significantly at the KAQZ. The highest OC, SOC/OC, and DA/OCs at Daliao (industrial area) were attributed to the transformation of primary VOCs to secondary OC via photo-oxidation during the episode. Oxalic acid was mainly produced through photochemical reactions since a high correlation between oxalic acid and Ca2+ was observed at Nanzi (urban area) and Daliao during the episode. During the episode, PM2.5 mostly originated from local primary or secondary aerosol than long-range overseas transport. The dominant source was anthropogenic emissions, accounting for 67.1% and 70.4% of PM2.5 at Nanzi and Daliao, respectively. At Chaozhou, the contribution of anthropogenic emissions was the lowest (42.4%), but secondary aerosols had the highest contribution of 38.3% of PM2.5 among the three areas during the episode.     

潍坊市环境受体中PM2.5污染特征与精细化来源解析

为掌握潍坊市PM2.5的主要来源、各排放源对PM2.5的贡献与内陆、沿海城市的差别,采集了潍坊市2017年不同季节环境受体中PM2.5样品和源样品,分析了样品中的化学组分,建立了源成分谱和受体组分数据库,基于复合受体模型和源排放量等对潍坊市PM2.5进行了来源解析。结果表明:(1)PM2.5和化学组分浓度总体表现为秋冬季较高、春夏季较低。(2)潍坊市源解析结果总体介于沿海城市和内陆城市之间。(3)精细化源解析表明:煤烟尘是首要的贡献源类,其分担率达到36.0%,其中电厂、工业、民用燃煤的分担率分别为14.4%、18.0%和3.6%;机动车尘的分担率达到25.4%,其中载客、载货、其他汽车的分担率分别为6.3%、14.0%和5.1%;扬尘中土壤风沙尘、建筑水泥尘的分担率分别为10.1%和11.7%;工艺过程的贡献相对较低(3.9%)。     

Contribution of railway traffic to local PM10 concentrations in Switzerland

Field measurement campaigns of PM10 and its elemental composition (daily sampling on filters) covering different seasons were performed at two sites near the busiest railway station of Switzerland in Zurich (at a distance of 10 m from the tracks) and at a site near a very busy railway line with more than 700 trains per day. At this latter site parallel samples were taken at 10, 36 and 120 m distances from the tracks with the aim to study the distance dependence of the railway induced PM10 concentrations.To distinguish the relatively small railway emissions from the regional background (typically 20–25 μg m⁻³), simultaneous samples were also taken at an urban background site in Zurich. The differences in PM10 and elemental concentrations between the railway exposed sites and the background site were allocated to the railway contribution.Small, however, measurable PM10 concentration differences were found at all sites. The elemental composition of these differences revealed iron as the only quantitatively important constituent. As a long-term average it amounted to approximately 1 μg m⁻³ Fe at a distance of 10 m from the tracks at all three sites. Assuming that iron was at least partly oxidised (e.g. in the form of Fe₂O₃) the contribution can amount up to 1.5 μg m⁻³. Emissions of copper, manganese and chromium from trains were also clearly identified. However, compared to iron these, elements were emitted in very low quantities.No significant contribution from rock material (calcium, aluminium, magnesium, sodium) was observed as might have been expected from erosion, abrasion and resuspension from the gravel below the tracks. Particle emissions from diesel exhaust were not considered as trains in Switzerland are operated nearly exclusively by electric locomotives.The railway, induced contribution to ambient PM10 decreased rapidly with increasing distance from the tracks. At a distance of 120 m this contribution dropped to only 25% of the contribution observed at 10 m distance.     

Source apportionment of wintertime secondary organic aerosol during the California regional PM10/PM2.5 air quality study

The UCD/CIT air quality model with the Caltech Atmospheric Chemistry Mechanism (CACM) was used to predict source contributions to secondary organic aerosol (SOA) formation in the San Joaquin Valley (SJV) from December 15, 2000 to January 7, 2001. The predicted 24-day average SOA concentration had a maximum value of 4.26 μg m^?3 50 km southwest of Fresno. Predicted SOA concentrations at Fresno, Angiola, and Bakersfield were 2.46 μg m^?3, 1.68 μg m^?3, and 2.28 μg m^?3, respectively, accounting for 6%, 37%, and 4% of the total predicted organic aerosol. The average SOA concentration across the entire SJV was 1.35 μg m^?3, which accounts for approximately 20% of the total predicted organic aerosol. Averaged over the entire SJV, the major SOA sources were solvent use (28% of SOA), catalyst gasoline engines (25% of SOA), wood smoke (16% of SOA), non-catalyst gasoline engines (13% of SOA), and other anthropogenic sources (11% of SOA). Diesel engines were predicted to only account for approximately 2% of the total SOA formation in the SJV because they emit a small amount of volatile organic compounds relative to other sources. In terms of SOA precursors within the SJV, long-chain alkanes were predicted to be the largest SOA contributor, followed by aromatic compounds. The current study identifies the major known contributors to the SOA burden during a winter pollution episode in the SJV, with further enhancements possible as additional formation pathways are discovered.     

Analysis of the PM2.5 emission inventory and source apportionment in Jilin City,Northeast of China

Environmental Science and Pollution Research - This study collected and compiled statistical data on atmospheric pollution in Jilin City, China during 2013–2014, using models and methods to...     

Estimating the emission source reduction of PM10 in central Taiwan

Three theoretical parent frequency distributions; lognormal, Weibull and gamma were used to fit the complete set of PM10 data in central Taiwan. The gamma distribution is the best one to represent the performance of high PM10 concentrations. However, the parent distribution sometimes diverges in predicting the high PM10 concentrations. Therefore, two predicting methods, Method I: two-parameter exponential distribution and Method II: asymptotic distribution of extreme value, were used to fit the high PM10 concentration distributions more correctly. The results fitted by the two-parameter exponential distribution are better matched with the actual high PM10 data than that by the parent distributions. Both of the predicting methods can successfully predict the return period and exceedances over a critical concentration in the future year. Moreover, the estimated emission source reductions of PM10 required to meet the air quality standard by Method I and Method II are very close. The estimated emission source reductions of PM10 range from 34% to 48% in central Taiwan.