2021
DOI: 10.21203/rs.3.rs-431378/v1
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Fine Particulate Matter Pollution Characteristics and Source Apportionment of Changchun Atmosphere

Abstract: In order to study the pollution characteristics and main sources of fine particulate matter in the atmosphere of the city of Changchun, PM2.5 samples were collected during the four seasons in 2014, and representative months for each season are January, April, July, and October. Sample collection was carried out on 10 auto-monitoring stations in Changchun, and PM2.5 mass concentration, and its chemical components (including inorganic elements, organic carbon, elemental carbon, and water-soluble ions) were measu… Show more

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Cited by 2 publications
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“…Analogously, An et al estimated the contribution of industrial sectors to PM 2.5 emissions in the Yangtze River Delta, utilizing an updated pollutant emission inventory and local measurement data [22]. However, these studies have relied on large-scale data for the spatial distribution of emissions and need help to locate emission sources accurately [23]. Source apportionment and numerical simulation methods elucidate the physical mechanisms behind pollutant formation and transport.…”
Section: Introductionmentioning
confidence: 99%
“…Analogously, An et al estimated the contribution of industrial sectors to PM 2.5 emissions in the Yangtze River Delta, utilizing an updated pollutant emission inventory and local measurement data [22]. However, these studies have relied on large-scale data for the spatial distribution of emissions and need help to locate emission sources accurately [23]. Source apportionment and numerical simulation methods elucidate the physical mechanisms behind pollutant formation and transport.…”
Section: Introductionmentioning
confidence: 99%
“…For the source apportionment of heavy metal(loid)s, Irving et al [17] first proposed a model to clarify the contribution of emission sources to receptors in polluted areas. Receptor models include the chemical-statistical method, physical method, and microscopic method, among which the chemical-statistical method is the most mature and widely used [18]. Chemical-statistical methods include geostatistical analysis, principal component analysis (PCA), factor analysis (FA), absolute principal component scores (APCS), multiple linear regression (MLR), chemical mass balance (CMB), redundancy analysis (RA), self-organizing maps (SOM), UNMIX modeling, random forest (RF), support vector machine (SVM), isotope labeling method, double source analysis method, back-propagation (BP) neural network, Fisher discriminant analysis, and cluster analysis (CA) [3,[19][20][21][22].…”
Section: Introductionmentioning
confidence: 99%