Chemical composition of PM2.5 particles in Salamanca, Guanajuato Mexico: Source apportionment with receptor models

Herrera-Murillo, Jorge; Ramos, Arturo Campos; García, Felipe Ángeles; Jiménez, Salvador Blanco; Cárdenas, B.; Mizohata, Akira

doi:10.1016/j.atmosres.2011.12.010

Cited by 52 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detailed information on the receptor modelling methods used within this study can be found elsewhere (Paatero and Tapper, 1994;Hopke, 2016). PMF is a multivariate factor analysis tool and based on a weighted least-squares fit, where the weights are derived from the analytical uncertainty.…”

Section: Positive Matrix Factorizationmentioning

confidence: 99%

“…Receptor models are widely used for source apportionment of PM 2.5 . These methods include positive matrix factorization (PMF) (Paatero, 1997;Paatero and Tapper, 1994), principal component analysis (PCA) (Lee and Hieu, 2011), chemical mass balance (CMB) (Watson et al, 1990), and UNMIX (Herrera Murillo et al, 2012). Among these methods, PMF is a widely used multivariate method that can resolve the dominant positive factors without prior knowledge of sources.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Insight into PM2.5 sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Srivastava

et al. 2021

Atmos. Chem. Phys.

View full text Add to dashboard Cite

Abstract. This study presents the source apportionment of PM2.5 performed by positive matrix factorization (PMF) on data presented here which were collected at urban (Institute of Atmospheric Physics – IAP) and rural (Pinggu – PG) sites in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9 November to 11 December 2016 and from 22 May to 24 June 2017. The PMF analysis included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted as traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion, and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (IAP: 22 %; PG: 24 %), biomass burning (IAP: 36 %; PG: 30 %), and coal combustion (IAP: 20 %; PG: 21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %), and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. chemical mass balance – CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry, AMS) and showed good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2= 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.

show abstract

Section: Positive Matrix Factorizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Insight into PM2.5 sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Srivastava

et al. 2021

Atmos. Chem. Phys.

View full text Add to dashboard Cite

show abstract

“…Receptor models are widely used for source apportionment of PM2.5. These methods include positive matrix factorization (PMF) (Paatero, 1997;Paatero and Tapper, 1994), principal component analysis (PCA) (Lee et al, 2011), chemical mass balance (CMB) (Watson et al, 1990), and UNMIX (Herrera Murillo et al, 2012). Among these methods, PMF is a widely used multivariate method that can resolve the dominant positive factors without prior knowledge of sources.…”

Section: Introductionmentioning

confidence: 99%

Insight into PM2.5 Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Srivastava¹,

Xu²,

Vu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. This study presents the source apportionment of PM2.5 performed by PMF on data collected at an urban (Institute of Atmospheric Physics – IAP) and a rural site (Pinggu-PG) in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9th November to 11th December 2016 and 22nd May to 24th June 2017. The PMF included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted to be traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (22–24 %), biomass burning (30–36 %), and coal combustion (20–21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %) and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry (AMS)) and showed a good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2 = 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS, and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.

show abstract

“…The region is inhabited by approximately 20 million people distributed in several major cities (e.g., Guadalajara, Leon, Aguascalientes, and Morelia) and is recognized for its large-scale industrial and agricultural activities. The MAQ is located 200 km north of Mexico City and is one of the fastest growing urban areas in the country, with a population of approximately 1.5 million in 2020. , In addition to local industry and traffic, the MAQ is surrounded by industrial parks, which include fertilizer plants and other medium and small industries, such as textiles, metal processing, and food, as well as a refinery and a power plant in the city of Salamanca. In Salamanca, previous studies have identified secondary aerosol, heavy fuel combustion, and traditional brick and biomass burning production as the main sources of PM .…”

Section: Introductionmentioning

confidence: 99%

Source Apportionment of Particulate Matter in the Metropolitan Area of Querétaro (Central Mexico): First Case Study

Olivares-Salazar

Álvarez-Ospina

Aguillon-Vazquez

et al. 2021

ACS Earth Space Chem.

View full text Add to dashboard Cite

Exposure to particulate matter (PM) represents an important health risk in urban areas because it is associated with cardiovascular and respiratory diseases. Knowledge of PM composition and sources is essential in the design of policies to reduce the burden of airborne particles. Although several urban areas in Mexico struggle with maintaining good air quality, little is known about the processes that determine the presence of pollutants in the atmosphere beyond Mexico City. Here, we describe the chemical composition of PM10 and PM2.5 in the metropolitan area of Querétaro (in Central Mexico) and identify the major emission sources and contributions using positive matrix factorization. We used, combined, and compared different data sets to gain more insight into the sources found. Three sources were found for PM10: resuspension of mineral and construction dust, an anthropogenic source with incineration, industrial, and secondary particles, and biomass burning. For PM2.5, the sources found include resuspension, anthropogenic (with incineration and industrial particles), vehicular, biomass burning, and secondary. This study is the first to investigate the chemical composition and source apportionment of PM10 and PM2.5 in the metropolitan area of Querétaro. Although some of the sources found are probably local, the regional contribution to the PM concentration is significant, thus revealing the importance of studying the central region of Mexico, which is an important industrial and agricultural area, with 20 million people distributed in several major urban areas, in an integrated way.

show abstract

Chemical composition of PM2.5 particles in Salamanca, Guanajuato Mexico: Source apportionment with receptor models

Cited by 52 publications

References 33 publications

Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM<sub>2.5</sub> Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Source Apportionment of Particulate Matter in the Metropolitan Area of Querétaro (Central Mexico): First Case Study

Contact Info

Product

Resources

About

Chemical composition of PM2.5 particles in Salamanca, Guanajuato Mexico: Source apportionment with receptor models

Cited by 52 publications

References 33 publications

Insight into PM&lt;sub&gt;2.5&lt;/sub&gt; sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM&lt;sub&gt;2.5&lt;/sub&gt; sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM&lt;sub&gt;2.5&lt;/sub&gt; Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Source Apportionment of Particulate Matter in the Metropolitan Area of Querétaro (Central Mexico): First Case Study

Contact Info

Product

Resources

About

Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM<sub>2.5</sub> Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing