Source apportionment of PM2.5 during haze episodes in Shanghai by the PMF model with PAHs

2021 ◽  
pp. 129850
Author(s):  
Xinxin Feng ◽  
Yanli Feng ◽  
Yingjun Chen ◽  
Junjie Cai ◽  
Qing Li ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Pmf Model

scholarly journals First-Time Source Apportionment Analysis of Deposited Particulate Matter from a Moss Biomonitoring Study in Northern Greece

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 208 ◽  
Author(s):  
Chrysoula Betsou ◽  
Evangelia Diapouli ◽  
Evdoxia Tsakiri ◽  
Lambrini Papadopoulou ◽  
Marina Frontasyeva ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Power Plants ◽  
Soil Dust ◽  
Northern Greece ◽  
Mining Operations ◽  
Moss Species ◽  
Elemental Concentrations ◽  
Pmf Model ◽  
Moss Biomonitoring ◽  
High Concentrations

Moss biomonitoring is a widely used technique for monitoring the accumulation of trace elements in airborne pollution. A total of one hundred and five samples, mainly of the Hypnum cupressiforme Hedw. moss species, were collected from the Northern Greece during the 2015/2016 European ICP Vegetation (International Cooperative Program on Effects of Air Pollution on Natural Vegetation and Crops) moss survey, which also included samples from the metalipherous area of Skouries. They were analyzed by means of neutron activation analysis, and the elemental concentrations were determined. A positive matrix factorization (PMF) model was applied to the results obtained for source apportionment. According to the PMF model, five sources were identified: soil dust, aged sea salt, road dust, lignite power plants, and a Mn-rich source. The soil dust source contributed the most to almost all samples (46% of elemental concentrations, on average). Two areas with significant impact from anthropogenic activities were identified. In West Macedonia, the emissions from a lignite power plant complex located in the area have caused high concentrations of Ni, V, Cr, and Co. The second most impacted area was Skouries, where mining activities and vehicular traffic (probably related to the mining operations) led to high concentrations of Mn, Ni, V, Co, Sb, and Cr.

scholarly journals Rainwater Chemistry Reveals Air Pollution in a Karst Forest: Temporal Variations, Source Apportionment, and Implications for the Forest

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1315
Author(s):  
Jie Zeng ◽  
Guilin Han
Keyword(s):  
Air Pollution ◽  
Source Apportionment ◽  
Environmental Effects ◽  
Deposition Process ◽  
Temporal Variations ◽  
Potential Effect ◽  
Rainwater Chemistry ◽  
Forest Park ◽  
Karst Forest ◽  
Pmf Model

Temporal rainwater chemistry was used to reveal air pollution in the Maolan National Karst Forest Park (MNKFP), which is representative of the typical karst forest region of southwest China (SW China). The rainwater ions’ sources, variations, trends, and potential environmental effects were investigated from 2007 to 2010 and from 2013 to 2014. Based on the analysis of the temporal ionic concentrations of rainwater in the MNKFP, significant variations of ions were observed, including in NH4+ (9.7~266.6 μeq L−1) and SO42− (14.5~1396.4 μeq L−1), which were mainly controlled by variations in the source and rainfall amount; a decreased trend of rainwater pH was also observed. Accordingly, NH4+, Ca2+, SO42−, and Cl− were regarded as the most dominant ions. Typical ionic ratios and positive matrix factorization (PMF) model-based source apportionment suggested that anthropogenic inputs (coal combustion, industrial, traffic, and agricultural emissions) contributed 51% of F−, 93% of NO3−, 62% of SO42−, and 87% of NH4+, while the natural sources (crustal dust and sea salt) were the main sources of Cl− (74%), Na+ (82%), K+ (79%), Mg2+ (94%), and Ca2+ (93%). In combination with the reducing neutralization trend of temporal rainwater observed in the MNKFP and the potential effect of rainwater ion deposition on karst forests, more detailed monitoring of the rainfall-related deposition process is required for a better understanding of its potential environmental effects on the Earth’s surface.

scholarly journals An Integrated Method for Factor Number Selection of PMF Model: Case Study on Source Apportionment of Ambient Volatile Organic Compounds in Wuhan

Atmosphere ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 390 ◽  
Author(s):  
Fenjuan Wang ◽  
Zhenyi Zhang ◽  
Costanza Acciai ◽  
Zhangxiong Zhong ◽  
Zhaokai Huang ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Source Apportionment ◽  
Correlation Coefficient ◽  
Organic Compounds ◽  
Industrial Emissions ◽  
Integrated Method ◽  
Pmf Model ◽  
Volatile Organic ◽  
Measurement Period

The positive matrix factorization (PMF) model is widely used for source apportionment of volatile organic compounds (VOCs). The question about how to select the proper number of factors, however, is rarely studied. In this study, an integrated method to determine the most appropriate number of sources was developed and its application was demonstrated by case study in Wuhan. The concentrations of 103 ambient volatile organic compounds (VOCs) were measured intensively using online gas chromatography/mass spectrometry (GC/MS) during spring 2014 in an urban residential area of Wuhan, China. During the measurement period, the average temperature was approximately 25 °C with very little domestic heating and cooling. The concentrations of the most abundant VOCs (ethane, ethylene, propane, acetylene, n-butane, benzene, and toluene) in Wuhan were comparable to other studies in urban areas in China and other countries. The newly developed integrated method to determine the most appropriate number of sources is in combination of a fixed minimum threshold value for the correlation coefficient, the average weighted correlation coefficient of each species, and the normalized minimum error. Seven sources were identified by using the integrated method, and they were vehicular emissions (45.4%), industrial emissions (22.5%), combustion of coal (14.7%), liquefied petroleum gas (LPG) (9.7%), industrial solvents (4.4%), and pesticides (3.3%) and refrigerants. The orientations of emission sources have been characterized taking into account the frequency of wind directions and contributions of sources in each wind direction for the measurement period. It has been concluded that the vehicle exhaust contribution is greater than 40% distributed in all directions, whereas industrial emissions are mainly attributed to the west southwest and south southwest.

scholarly journals Source Apportionment of PM2.5 in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 512
Author(s):  
Tingting Li ◽  
Jun Li ◽  
Hongxing Jiang ◽  
Duohong Chen ◽  
Zheng Zong ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Matrix Factorization ◽  
Coal Combustion ◽  
Mixing Model ◽  
Positive Matrix ◽  
Traffic Emission ◽  
Bayesian Mixing Model ◽  
Pmf Model ◽  
Aerosol Source ◽  
Biogenic Source

To accurately apportion the sources of aerosols, a combined method of positive matrix factorization (PMF) and the Bayesian mixing model was applied in this study. The PMF model was conducted to identify the sources of PM2.5 in Guangzhou. The secondary inorganic aerosol source was one of the seven main sources in Guangzhou. Based on stable isotopes of oxygen and nitrogen (δ15N-NO3− and δ18O-NO3−), the Bayesian mixing model was performed to apportion the source of NO3− to coal combustion, traffic emission and biogenic source. Then the secondary aerosol source was subdivided into three sources according to the discrepancy in source apportionment of NO3− between PMF and Bayesian mixing model results. After secondary aerosol assignment, the six main sources of PM2.5 were traffic emission (30.6%), biomass burning (23.1%), coal combustion (17.7%), ship emission (14.0%), biomass boiler (9.9%) and industrial emission (4.7%). To assess the source apportionment results, fossil/non-fossil source contributions to organic carbon (OC) and element carbon (EC) inferred from 14C measurements were compared with the corresponding results in the PMF model. The results showed that source distributions of EC matched well between those two methods, indicating that the PMF model captured the primary sources well. Probably because of the lack of organic molecular markers to identify the biogenic source of OC, the non-fossil source contribution to OC in PMF results was obviously lower than 14C results. Thus, an indicative organic molecular tracer should be used to identify the biogenic source when accurately apportioning the sources of aerosols, especially in the region with high plant coverage or intense biomass burning.

Sediment PAH source apportionment in the Liaohe River using the ME2 approach: A comparison to the PMF model

2016 ◽  
Vol 553 ◽  
pp. 164-171 ◽  
Author(s):  
Jian Xu ◽  
Xing Peng ◽  
Chang-Sheng Guo ◽  
Jiao Xu ◽  
Hai-Xia Lin ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Liaohe River ◽  
Pmf Model

scholarly journals SoFi, an Igor based interface for the efficient use of the generalized multilinear engine (ME-2) for source apportionment: application to aerosol mass spectrometer data

2013 ◽  
Vol 6 (4) ◽  
pp. 6409-6443 ◽  
Author(s):  
F. Canonaco ◽  
M. Crippa ◽  
J. G. Slowik ◽  
U. Baltensperger ◽  
A. S. H. Prévôt
Keyword(s):  
Source Apportionment ◽  
Mass Spectra ◽  
A Priori ◽  
Aerosol Mass Spectrometer ◽  
Pmf Model ◽  
Aerosol Mass ◽  
Five Factors ◽  
Multilinear Engine ◽  
Constrained Models ◽  
Spectrometer Data

Abstract. Source apportionment using the bilinear model through the multilinear engine (ME-2) was successfully applied to non-refractory organic aerosol (OA) mass spectra collected during winter 2011 and 2012 in Zurich, Switzerland using the aerosol chemical speciation monitor ACSM. Five factors were identified: low-volatility oxygenated OA (LV-OOA), semivolatile oxygenated OA (SV-OOA), hydrocarbon-like OA (HOA), cooking OA (COA) and biomass burning OA (BBOA). A graphical user interface SoFi (Source Finder) was developed at PSI in order to facilitate the testing of different rotational techniques available within the ME-2 engine by providing a priori factor profiles for some or all of the expected factors. ME-2 was used to test the positive matrix factorization (PMF) model, the fully constrained chemical mass balance (CMB) model, and partially constrained models utilizing a values and pulling equations. Within the set of model solutions determined to be environmentally reasonable, BBOA and SV-OOA factor mass spectra and time series showed the greatest variability. This variability represents uncertainty in the model solution and indicates that analysis of model rotations provides a useful approach for assessing the uncertainty of bilinear source apportionment models.

scholarly journals Understanding primary and secondary sources of ambient carbonyl compounds in Beijing using the PMF model

2013 ◽  
Vol 13 (6) ◽  
pp. 15749-15781
Author(s):  
W. T. Chen ◽  
M. Shao ◽  
S. H. Lu ◽  
M. Wang ◽  
L. M. Zeng
Keyword(s):  
Source Apportionment ◽  
Carbonyl Compounds ◽  
Anthropogenic Sources ◽  
Positive Matrix ◽  
Secondary Sources ◽  
Pmf Model ◽  
Five Factors ◽  
Secondary Formation ◽  
Volatile Organic ◽  
Peking University

Abstract. Carbonyls are important intermediates in atmospheric photochemistry. To determine the relative contributions of primary and secondary carbonyl sources in Beijing, carbonyls and other trace gases were measured at Peking University in winter and summer. The Positive Matrix Factorization (PMF) model was used for source apportionment. As volatile organic compounds (VOCs) will undergo photochemical processes in the atmosphere, and such processes may interfere with factors identification, the relationships between the contributions of the resolved PMF factors to each non-methane hydrocarbon (NMHC) species and its kOH value were used to distinguish between photochemically aged factors and fresh factors. As the result of PMF, five factors were resolved in winter, and two of them were identified as sources of photochemically aged emissions. In summer, four factors were resolved, including an aged factor. Carbonyls in the aged factors were simulated by NMHCs consumption and the corresponding carbonyl production yields, and the simulated abundances agreed well with the results obtained by PMF. The source apportionment results indicated that secondary formation was the major source of carbonyls in both seasons, with the contribution of 51.2% and 46.0%. For the three major carbonyl species, primary anthropogenic sources contributed 28.9% and 32.3% to ambient formaldehyde, 53.7% and 41.6% to acetaldehyde, 68.1% and 56.2% to acetone in winter and summer, respectively.

Source apportionment of ambient PM2.5 in two locations in central Tehran using the Positive Matrix Factorization (PMF) model

2018 ◽  
Vol 628-629 ◽  
pp. 672-686 ◽  
Author(s):  
Sina Taghvaee ◽  
Mohammad H. Sowlat ◽  
Amirhosein Mousavi ◽  
Mohammad Sadegh Hassanvand ◽  
Masud Yunesian ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Matrix Factorization ◽  
Positive Matrix Factorization ◽  
Positive Matrix ◽  
Pmf Model

scholarly journals Introduction and Application of the PMF Model to Estimate the Source Apportionment of PM2.5 at Various Sites

1970 ◽  
Vol 8 (3) ◽  
pp. 25-31 ◽  
Author(s):  
Injo Hwang
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Receptor Site ◽  
Ambient Air ◽  
Receptor Modeling ◽  
Ambient Air Quality ◽  
Receptor Models ◽  
Pmf Model ◽  
Source Contributions ◽  
Reduction Strategies

To manage ambient air quality and establish effective emissions reduction strategies, it is necessary to identify sources and to apportion the ambient PM mass. To do so, receptor models have been developed that analyze various measured properties of the pollutants at the receptor site, identify the sources, and estimate their contributions. Receptor modeling is based on a mathematical model that analyzes the physicochemical properties of gaseous and/or particulate pollutants at various atmospheric receptors. Among the multivariate receptor models used for PM source identification and apportionment, positive matrix factorization (PMF) has been developed by Paatero in 1997. PMF have been developed for providing a new approach to multivariate receptor modeling based on explicit least-squares technique. Also, PMF shown to be a powerful technique relative to traditional multivariate receptor models. PMF has been implemented in two different algorithms: PMF2 (or PMF3) and the multilinear engine (ME). Since the release of PMF2 and ME, these programs have been successfully applied to assess ambient PM source contributions at many locations in the world. In this study, I would like to introduce about outline of the PMF model and application of the PMF model to estimate the source apportionment of ambient PM2.5 at various sampling sites in USA and Korea. This study suggests the possible role for maintain and manage ambient air quality and achieve reasonable air pollution strategies. DOI: http://dx.doi.org/10.3126/jie.v8i3.5928 JIE 2011; 8(3): 25-31

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