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Elemental analysis of PM10 in southwest Mexico City and source apportionment using positive matrix factorization

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Abstract

The results of a study of the elemental concentrations in PM10 samples collected at a site in southwest Mexico City during 2016 and 2019, are presented. The concentrations of up to 19 elements were measured with X-ray fluorescence (XRF). These analyses were complemented with ion chromatography for eight ionic species (for the samples collected in 2016). The behaviors of the gravimetric mass and elemental concentrations are described for the morning, afternoon, and night-time periods in 2019. The elemental concentrations observed in the PM10 samples did not present significant changes as compared to those published in previous works. It was found that the gravimetric mass concentrations were always below the official standards, except during a contingency period in May 2019. The positive matrix factorization (PMF) receptor model was used to identify contaminating sources and their relative contributions to the concentrations of the detected elements. The soil-related factors were the most abundant contributors, with other components associated to traffic, biomass burning, fuel oil, secondary aerosol, and dust resuspension. The occurrence of episodes in 2019 is explained with the aid of PMF and back-trajectories, while the contingency period is due to other chemical species not detected in PM10 with XRF. A comparison with data collected in 2005 in downtown Mexico City is also carried out, as well as with urban areas in other countries.

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Fig. 1

Source: Google Earth®)

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Source profiles for 2016 as determined with PMF receptor model

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Source profiles for 2019 as determined with PMF receptor model

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Source profiles for 2005 as determined with PMF receptor model

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Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors acknowledge the technical assistance of M.C. Torres-Barrera and A.L. Alarcón. Additionally, the support of Dr. T. Castro, Dr. A. Jazcilevich, Dr. C. Solís, and Dr. R. Sosa is appreciated. This work was supported in part by DGAPA-UNAM, under grants IN-102615 and IN-101719, as well as CONACyT 253051. AEHL, LVMP, SRC and JAMF recognize the support of CONACyT through scholarships. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (https://www.ready.noaa.gov) used in this publication. Additionally, the use of imagery from the NASA FIRMS application (http://firms.modaps.eosdis.nasa.gov/) operated by the NASA/Goddard Space Flight Center Earth Science Data and Information System (ESDIS) project is recognized.

Funding

This work was supported in part by Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México, under grants IN-102615 and IN-101719, as well as Consejo Nacional de Ciencia y Tecnología under grant 253051. AEHL, LVMP, SRC and JAMF recognize the support of CONACyT through scholarships.

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Authors and Affiliations

  1. Departamento de Física Nuclear Y Aplicaciones de La Radiación, Instituto de Física, Universidad Nacional Autónoma de México, Circuito Investigación Científica S/N, Ciudad Universitaria, Cd. Mx, 04510, Coyoacán, Mexico

    Lourdes Verónica Mejía-Ponce, Alfonso Enrique Hernández-López, Javier Miranda-Martín-del-Campo, Juan Carlos Pineda-Santamaría, Salvador Reynoso-Cruces & Juan Antonio Mendoza-Flores

  2. CONACyT, Centro de Investigación en Corrosión, Universidad Autónoma de Campeche, San Francisco de Campeche, Av. Héroe de Nacozari # 480, 24070, Campeche, Mexico

    Alberto Antonio Espinosa-Guzmán

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  1. Lourdes Verónica Mejía-Ponce
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Contributions

LVMP and AEHL participated in the design of the campaign, collected and analyzed samples, assisted in the receptor model application, read and corrected the manuscript; JM designed the study, participated in the design of the campaign, analyzed samples, assisted in the receptor model application, and wrote the first version of the manuscript; SRC, JAMF, and JCP collected and analyzed samples; AAE performed the 2005 study, assisted in the receptor model application, read and corrected the manuscript.

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Correspondence to Javier Miranda-Martín-del-Campo.

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Appendix

Appendix

In this appendix, Fig. 18 presents a graph for the comparison of PM2.5/PM10 ratios obtained during 2016 and 2019 at the southwest site with the 2005 ratios collected at the downtown site; Fig. 19 displays a comparison of the Na, Mg, Cl, K, and Ca concentrations measured with XRF and IC for the 2016 samples; Fig. 20 shows the linear dependence between the wind direction and Mg, Ca, Zn, and K elemental concentrations; a plot demonstrating the linear relationship between NH4+ and the sulfate factor during the 2016 campaign is displayed in Fig. 21. Additionally, the back-trajectories obtained with HYSPLIT (Rolph et al. 2017; Stein et al. 2015), for April 29, 2019, are shown in Fig. 22.

The application of ANOVA to the three data sets showed there was no significant difference in the PM2.5/PM10 ratios among the data sets (Fig. 18).

As the concentrations of the ionic species related to Na, Mg, Cl, K, and Ca measured with ion chromatography had mean values equal to those of the concentrations measured with XRF, within experimental uncertainties (Fig. 19), it was more convenient to use the latter values for PMF.

Significant negative linear correlations were found between the concentrations of several elements during the afternoon (Mg, Ca, Zn), and the night (K) periods during 2019, as shown in Fig. 20. This suggests the presence of emitting sources in the directions opposed to wind provenance.

The highly significant correlation between NH4+ and the PMF-obtained sulfate factor, displayed in Fig. 21, supports the identification of this source as secondary aerosol.

Additionally, the backtrajectories shown in Fig. 22 demonstrate the transport of pollutants from the Tula region (with operational thermoelectric power plants and oil refineries located in the zone) and those emitted by biomass burning locations, as determined with the FIRMS database (Justice et al. 2002).

Fig. 18
figure 18

Comparison among the PM2.5/PM10 ratios obtained during 2016 and 2019 in the southwest site and the 2005 ratios obtained in downtown

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Fig. 19
figure 19

Comparison between Na, Mg, Cl, K, and Ca elemental concentrations measured with XRF and IC in the 2016 samples

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Fig. 20
figure 20

Dependence of Mg, Ca, Zn (afternoon samples) and K (night samples) concentrations with wind direction, for the 2019 campaign

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Fig. 21
figure 21

Concentrations of the ionic species NH4+ as a function of the sulfate factor concentrations, as calculated through PMF

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Fig. 22
figure 22

Back-trajectories calculated with HYSPLIT (Rolph et al. 2017; Stein et al. 2015) for April 29, 2019, with the UNAM site as the receptor. The red squares represent fire spots located through the FIRMS database (Justice et al. 2002)

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Table 6 Mean concentrations of ionic species in PM10, collected at southwest in 2016(µg m-3)
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Mejía-Ponce, L.V., Hernández-López, A.E., Miranda-Martín-del-Campo, J. et al. Elemental analysis of PM10 in southwest Mexico City and source apportionment using positive matrix factorization. J Atmos Chem 79, 167–198 (2022). https://doi.org/10.1007/s10874-022-09435-2

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