PM2.5 source apportionment: reconciling receptor models for U.S. nonurban and urban long-term networks

Chemical mass balance (CMB) and trajectory receptor models were applied to speciated particulate matter with aerodynamic diameter < or =2.5 microm (PM2.5) measurements from Speciation Trends Network (STN; part of the Chemical Speciation Network [CSN]) and Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network across the state of Minnesota as part of the Minnesota PM2.5 Source Apportionment Study (MPSAS). CMB equations were solved by the Unmix, positive matrix factorization (PMF), and effective variance (EV) methods, giving collective source contribution and uncertainty estimates. Geological source profiles developed from local dust materials were either incorporated into the EV-CMB model or used to verify factors derived from Unmix and PMF. Common sources include soil dust, calcium (Ca)-rich dust, diesel and gasoline vehicle exhausts, biomass burning, secondary sulfate, and secondary nitrate. Secondary sulfate and nitrate aerosols dominate PM2.5 mass (50-69%). Mobile sources outweigh area sources at urban sites, and vice versa at rural sites due to traffic emissions. Gasoline and diesel contributions can be separated using data from the STN, despite significant uncertainties. Major differences between MPSAS and earlier studies on similar environments appear to be the type and magnitude of stationary sources, but these sources are generally minor (<7%) in this and other studies. Ensemble back-trajectory analysis shows that the lower Midwestern states are the predominant source region for secondary ammoniated sulfate in Minnesota. It also suggests substantial contributions of biomass burning and soil dust from out-of-state on occasions, although a quantitative separation of local and regional contributions was not achieved in the current study. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of the Air & Waste Management Association for a summary of input data, Unmix and PMF factor profiles, and additional maps.     

Determination of single particle mass spectral signatures from heavy-duty diesel vehicle emissions for PM2.5 source apportionment

The size and chemical composition of individual diesel exhaust particles were measured in order to determine unique mass spectral signatures that can be used to identify particle sources in future ambient studies. The exhaust emissions from seven in-use heavy-duty diesel vehicles (HDDVs) operating on a chassis dynamometer were passed through a dilution tunnel and residence chamber and analyzed in real time by aerosol time-of-flight mass spectrometry (ATOFMS). Seven distinct particle types describe the majority of particles emitted by HDDVs and were emitted by all seven vehicles. The dominant chemical types originated from unburned lubricant oil, and the contributions of the various types varied with particle size and driving conditions. A comparison of light-duty vehicle (LDV) exhaust particles with the HDDV signatures provide insight into the challenges associated with developing an accurate source apportionment technique and possible ways of how they may be overcome.     

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.     

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.     

Inorganic PM2.5 at a U.S. agricultural site

In this study, we present approximately two years (January 1999-December 2000) of atmospheric NH3, NH4+, HCl, Cl-, HNO3, NO3-, SO2, and SO4= concentrations measured by the annular denuder/filter pack method at an agricultural site in eastern North Carolina. This site is influenced by high NH3 emissions from animal production and fertilizer use in the surrounding area and neighboring counties. The two-year mean NH3 concentration is 5.6 (+/-5.13) microg m(-3). The mean concentration of total inorganic PM2.5, which includes SO4=, NO3-, NH4+, and Cl-, is 8.0 (+/-5.84) microg m(-3). SO4=, NO3-, NH4+, and Cl- represent, respectively, 53, 24, 22, and 1% of measured inorganic PM2.5. NH3 contributes 72% of total NH3 + NH4+, on an average. Equilibrium modeling of the gas+aerosol NH3/H2SO4/HNO3 system shows that inorganic PM2.5 is more sensitive to reductions in gas + aerosol concentrations of sulfate and nitrate relative to NH3.     

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.     

Method comparisons for particulate nitrate, elemental carbon, and PM2.5 mass in seven U.S. cities

Methods that measure PM2.5 mass, total particulate NO3-, and elemental carbon (EC) were evaluated in seven U.S. cities from 1997 to 1999. Sampling was performed in Bakersfield, CA; Boston, MA; Chicago, IL; Dallas, TX; Philadelphia, PA; Phoenix, AZ; and Riverside, CA. Evaluating and validating methods that measure the components of fine mass are important to the effort of establishing a speciation-monitoring network. The Harvard Impactor (HI), which measures fine particle mass, showed excellent agreement (r2 = 0.99) with the PM2.5 Federal Reference Method (FRM) for 81 24-hr samples in Riverside and Bakersfield. The HI also showed good precision (4.8%) for 243 24-hr collocated samples over eight studies. The Aethalometer was employed in six of the sampling locations to measure black carbon (BC). These values were compared to EC as measured from a quartz filter using thermal analysis. For the six cities combined, the two methods were highly correlated (r2 = 0.94; 187 24-hr samples); however, the BC values were approximately 24% less than the EC measurements consistently across all six cites. This compares well to results observed for EC/BC measurements observed in other semi-urban areas. Particulate NO3- was measured using the Harvard-EPA Annular Denuder System (HEADS). This was compared to the NO3- measured from the HI Teflon (DuPont) filter to assess NO3- artifacts. Significant NO3- losses (approximately 50% of total NO3-) were found in Riverside, Philadelphia, and Boston, while minimal artifacts were observed in the other sites. Two types of HEADS configurations were employed in five cities. One system used a Na2CO3-coated glass fiber filter, and the other type used a nylon filter to collect volatilized NO3- from the Teflon filter. The HEADS with the Na2CO3-coated filter consistently underestimated the total particulate NO3- by approximately 20% compared to the nylon HEADS.     

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...     

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...     

Source apportionment: findings from the U.S. Supersites Program

Receptor models are used to identify and quantify source contributions to particulate matter and volatile organic compounds based on measurements of many chemical components at receptor sites. These components are selected based on their consistent appearance in some source types and their absence in others. UNMIX, positive matrix factorization (PMF), and effective variance are different solutions to the chemical mass balance (CMB) receptor model equations and are implemented on available software. In their more general form, the CMB equations allow spatial, temporal, transport, and particle size profiles to be combined with chemical source profiles for improved source resolution. Although UNMIX and PMF do not use source profiles explicitly as input data, they still require measured profiles to justify their derived source factors. The U.S. Supersites Program provided advanced datasets to apply these CMB solutions in different urban areas. Still lacking are better characterization of source emissions, new methods to estimate profile changes between source and receptor, and systematic sensitivity tests of deviations from receptor model assumptions.     

Chemical composition and mass closure of ambient PM10 at urban sites

The chemical composition of PM10 was studied during summer and winter sampling campaigns conducted at two different urban sites in the city of Thessaloniki, Greece (urban-traffic, UT and urban-industrial, UI). PM10 samples were chemically analysed for minerals (Si, Al, Ca, Mg, Fe, Ti, K), trace elements (Cd, Cr, Cu, Mn, Pb, V, Zn, Te, Co, Ni, Se, Sr, As, and Sb), water-soluble ions (Cl^?, NO₃^?, SO₄^2?, Na⁺, K⁺, NH₄⁺, Ca²⁺, Mg²⁺) and carbonaceous compounds (OC, EC). Spatial variations of atmospheric concentrations showed significantly higher levels of minerals, some trace metals and TC at the UI site, while at the UT site significantly higher levels of elements like Cd, Ba, Sn, Sb and Te were observed. Crustal elements, excepting Ca at the UI site, did not exhibit significant seasonal variations at any site pointing to constant emissions throughout the year. In order to reconstruct the particle mass, the determined components were classified into six classes as follows: mineral matter (MIN), trace elements (TE), organic matter (OM), elemental carbon (EC), sea salt (SS) and secondary inorganic aerosol (SIA). Good correlations with slopes close to 1 were found between chemically determined and gravimetrically measured PM10 masses for both sites. According to the chemical mass closure obtained, the major components of PM10 at both sites were MIN (soil-derived compounds), followed by OM and SIA. The fraction unaccounted for by chemical analysis comprised on average 8% during winter and 15% during summer at the urban-industrial site, while at the urban-traffic site the percentages were 21.5% in winter and 4.8% in summer.     

Health risk assessment and source apportionment of PM2.5-bound toxic elements in the industrial city of Siheung,Korea

Environmental Science and Pollution Research - The emission sources and their health risks of fine particulate matter (PM2.5) in Siheung, Republic of Korea, were investigated as a middle-sized...     

Source apportionment of particulate matter (PM2.5) in an urban area using dispersion,receptor and inverse modelling

Air pollution emission inventories are the basis for air quality assessment and management strategies. The quality of the inventories is of great importance since these data are essential for air pollution impact assessments using dispersion models. In this study, the quality of the emission inventory for fine particulates (PM_2.5) is assessed: first, using the calculated source contributions from a receptor model; second, using source apportionment from a dispersion model; and third, by applying a simple inverse modelling technique which utilises multiple linear regression of the dispersion model source contributions together with the observed PM_2.5 concentrations. For the receptor modelling the chemical composition of PM_2.5 filter samples from a measurement campaign performed between January 2004 and April 2005 are analysed. Positive matrix factorisation is applied as the receptor model to detect and quantify the various source contributions. For the same observational period and site, dispersion model calculations using the Air Quality Management system, AirQUIS, are performed. The results identify significant differences between the dispersion and receptor model source apportionment, particularly for wood burning and traffic induced suspension. For wood burning the receptor model calculations are lower, by a factor of 0.54, but for the traffic induced suspension they are higher, by a factor of 7.1. Inverse modelling, based on regression of the dispersion model source contributions and the PM_2.5 concentrations, indicates similar discrepancies in the emissions inventory. In order to assess if the differences found at the one site are generally applicable throughout Oslo, the individual source category emissions are rescaled according to the receptor modelling results. These adjusted PM_2.5 concentrations are compared with measurements at four independent stations to evaluate the updated inventory. Statistical analysis shows improvement in the estimated concentrations for PM_2.5 at all sites. Similarly, inverse modelling is applied at these independent sites and this confirms the validity of the receptor model results.     

Composition analysis of PM2.5 at multiple sites in Zhengzhou,China: implications for characterization and source apportionment at different pollution levels

Environmental Science and Pollution Research - Zhengzhou is one of the most heavily polluted cities in China. This study collected samples of PM2.5 (atmospheric fine particulate matter with...     

Determination of buffering capacity of total suspended particle and its source apportionment using the chemical mass balance approach

The samples of total suspended particle (TSP) from sources and TSP in the ambient atmosphere were collected in 2006 at Tianjin, People's Republic of China and analyzed for 16 chemical elements, two water-soluble ions, total carbon, and organic carbon. On the basis of the chemical mass balance (CMB) model, the contributions of different TSP sources to the ambient TSP were identified. The results showed that resuspended dust has the biggest contributions to the concentration of ambient TSP. The buffering capacity of each TSP source was also determined by an analytical chemistry method, and the result showed that the constructive dust (the dust emitted from construction work) had the strongest buffering capacity among the measured sources, whereas the coal combustion dust had the weakest buffering capacity. A calculation formula of the source of buffering capacity of ambient TSP was developed based on the result of TSP source apportionment and the identification of the buffering capacity of each TSP source in this study. The results of the source apportionment of the buffering capacity of ambient TSP indicated that open sources (including soil dust, resuspended dust, and constructive dust) were the dominant sources of the buffering capacity of the ambient TSP. Acid rain pollution in cities in Northern China might become serious with a decrease of open source pollution without reducing acidic sources. More efforts must be made to evaluate this potential risk, and countermeasures should be proposed as early as possible.     

Seasonal variations and source apportionment of atmospheric PM2.5-bound polycyclic aromatic hydrocarbons in a mixed multi-function area of Hangzhou,China

Environmental Science and Pollution Research - To reveal the seasonal variations and sources of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) during haze and non-haze episodes, daily PM2.5...     

PM2.5-bound phthalates and phthalate substitutes in a megacity of southern China: spatioseasonal variations,source apportionment,and risk assessment

Environmental Science and Pollution Research - Plasticizers are ubiquitous pollutants in the environment, whereas few efforts have been made to elucidate their emission sources in the atmosphere....     

Measurements of PM10 and PM2.5 in urban area of Nanjing,China and the assessment of pulmonary deposition of particle mass

Measurement of PM10 and PM2.5 was carried out at six sites of Nanjing, China in the period of February-May 2001. The pH and conductivity of water-soluble matter of PM10 and PM2.5 were determined, and the samples were analyzed for total carbon (TC), organic carbon (OC) and inorganic carbon (IC) of the water-soluble fraction. The distribution of aerosol mass concentration in size was also measured at one site SB by a nine-stage impactor followed to assess the pulmonary deposition of particles in different tracts of the human respiratory system. Compared with National Ambient Air Quality Standard (NAAQS) of the USA, the level of PM10 and PM2.5 in Nanjing was much higher. Especially for site SY, the average particle mass concentrations (774.5 micrograms/m3 for PM10 and 481.4 micrograms/m3 for PM2.5) were more than five times the NAAQS standard. At site SB aerosol mass distribution in size had shown the similar characteristics with accumulation (Dp < 1 micron) and coarse (Dp > 1 micron) modes. More than 70% of total suspended particles is of a size that they are deposited in the respiratory tract below trachea, whereas about 22% of the mass is respirable and will reach the alveoli. Water-soluble fractions of PM10 and PM2.5 in Nanjing are acidic, and the pH of PM2.5 is lower than that of PM10. OC makes up the majority of TC and accounts for 3-14% of mass concentration of PM10 and/or PM2.5, while IC only accounts for 0.1-0.5% of PM10 and/or PM2.5 mass.     

Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2.5) forecasting

Multi-layer perceptron (MLP) artificial neural network (ANN) models are compared with traditional multiple regression (MLR) models for daily maximum and average O3 and particulate matter (PM10 and PM2.5) forecasting. MLP particulate forecasting models show little if any improvement over MLR models and exhibit less skill than do O3 forecasting models. Meteorological variables (precipitation, wind, and temperature), persistence, and co-pollutant data are shown to be useful PM predictors. If MLP approaches are adopted for PM forecasting, training methods that improve extreme value prediction are recommended.