Supplementary Data

Article title:Chemical composition and source apportionment of PM10 at an urban background site in a high–

altitude Latin American megacity (Bogota, Colombia)

Omar Ramíreza,b*, A.M. Sánchez de la Campaa, Fulvio Amatoc, Ruth A. Catacolíd, Néstor Y. Rojase,

Jesús de la Rosaa

a Associate Unit CSIC–University of Huelva “Atmospheric Pollution”, Centre for Research in

Sustainable Chemistry–CIQSO, Campus de El Carmen s/n, 21071, Huelva, Spain.b Environmental Engineering Program. Group of Applied Environmental Studies–GEAA.

Universidad Nacional Abierta y a Distancia–UNAD. Tv 31 #12–38 sur, Bogota, Colombia.c Institute for Environmental Assessment and Water Research (IDÆA-CSIC), C/Jordi Girona 18-26,

Barcelona, Spain.d Environmental Engineering Program. Universidad Libre. Cr. 70A # 53–40. Bogota, Colombia.e Department of Chemical and Environmental Engineering. Universidad Nacional de Colombia. Cr.

30 # 45–03. Edif. 412, Of. 206. Bogota, Colombia.

* Corresponding author. E–mail address: omar.ramirez@unad.edu.co.

Journal:Environmental Pollution

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Table SD1. Classification of chemical elements (PMF model).

Elements PMF Elements PMF Elements PMF

PM10 Weak (by default) Ti strong Pb strong

Al strong V strong Li strong

Ca strong Cr weak Se weak

K strong Co strong La strong

Na strong Ni weak Ba weak

Mg strong Cu strong Sc strong

Fe strong Zn weak Nb strong

PO43– weak As strong Cs weak

SO42– strong Ga strong Ce strong

NO3– strong Rb strong Pr weak

Cl– weak Sr strong Nd weak

NH4+ strong Cd strong other trace elementsi bad

OC strong Sn strong

EC strong Sb strongiGe, Mo, Zr, Hf, Be, Y, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Ta, W, Tl, Bi, Th, U.

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Table SD2. PM10 levels at urban background sites around the world. The first eight cities have a similar

population size to Bogota.

Country City Population sizei

Station or site of sampling Year PM10

(μg m–3) Reference

Colombia Bogota 8 081 000ii Universidad Libre 2015–2016 37.521.5 This study

Brazil Rio de Janeiro 6 499 000 Gávea 2003–2005 2111 Godoy et al., 2009

Chile Santiago de Chile 6 045 530 Las Condes 2014–2015 52.120.1 SINCA

Chile Santiago de Chile 6 045 530 Las Condes 2004–2005 51 to 77.9 Valdés et al.,

2013China Guangzhou 10 642 000 Jinan University 2011–2012 32 to 129 Wang et al., 2014

Mexico Ciudad de Mexico 8 918 650 Acolman 2014–2016 33.7 SEDEMA

Peru Lima 9 886 600 Campo de Marte 2010–2016 42.2 SENAMHI

Thailand Bangkok 8 305 200 Bang Na 2001–2003 57.6±23.9 Chuersuwan et al., 2008

USA New York 8 550 400 New York–Newark–Jersey City 2014–2016 18.7 EPA US

United Kingdom London 8 754 700 London N. Kensington 2010–2016 21.7 DEFRA

United Kingdom London 8 754 700 London N. Kensington 2002–2004 24.9±0.6 Jones and

Harrison, 2006

Argentina Buenos Aires 3 060 000 Coastal site 2006–2007 23±11 Arkouli et al., 2010

Belgium Antwerpen 517 049 Borgerhout 2010–2011 23 (18–32)iii Maenhaut et al., 2012

China Fuzhou 2 825 000 Gushan 2007–2008 23.92±6.13 to 41.93±6.93 Xu et al., 2012

China Beijing 16 447 000 Beijing Forestry University 2011–2012 62–245 Wang et al., 2014

Greece Athens 3 170 000 Agia Paraskevi 2007 22.7 to 32.5 Kassomenos et al., 2014

Hungary Budapest 1 760 000 Central Research Institute for Physics 2002 18–50 Salma et al., 2004

India Mumbai 18 395 000 Colaba 2007–2008 139.8±75.7 Gupta et al., 2012Ireland Dublin 553 200 Coleraine 2001–2002 22 Yin et al., 2005

Italy Torino 890 530 Lingotto – 33.7±18.8 Schilirò et al., 2015

Italy Lecce 95 000 ISAC–CNR 2007–2008 26.3±11.8 Contini et al., 2010

Lebanon Beirut 418 000 American University of Beirut 2012 38.4±2.3 Daher et al., 2013

Spain Madrid 3 142 000 Casa de Campos 2005 29.06–34.50 Kassomenos et al., 2014

Spain Barcelona 1 604 500 Palau Reial 2009–2011 24 to 31 Pérez et al., 2016South Korea Busan 3 449 000 Residential area 2014 47.1 Jang et al., 2017

United Kingdom Edinburgh 480 250 University Old College 1999–2000 14.2 (7.3–

29.1)iv Heal et al., 2005

United Kingdom Birmingham 1 127 000 City centre site 2004–2005 23.9±11.5 Yin and Harrison,

2008iData from: https://www.citypopulation.de. iiDANE, 2010. iii25%–75% interquartile ranges. iv5%–95% interquartile

ranges.

3

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

f(x) = 0.913535262042261 x − 1.22713664072788R² = 0.928208806573332

PM10 (gravimetric) μg m-3

PM10

(mas

s cl

osur

e) μ

g m

-3

Fig. SD1. PM10 gravimetric versus PM10 mass closure. The three most significant outliers (160 μg m–3, 140

μg m–3 and 103 μg m–3, corresponding to 01/20/2016, 06/13/2015 and 10/24/2015, respectively) were not

graphed because they appear only on PM10 gravimetric data, but not on PM10 mass closure.

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Fig. SD2. The Pearson coefficient matrix. DS = dry season, RS = rainy season. r–values are multiplied by

100. The correlation is coded in three ways: by shape (ellipses), color and the numeric value (Carslaw,

2015).

DS

RS

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Fig. SD3. a) Wind rose for the day with the highest concentration of F3 (non–ferrous metal smelting)

(01/03/2016), b) Polar plot of Cu concentration during the period with the highest concentration of F3

(12/15/2015–01/07/2016) based on daily data, c) Correlation matrix of trace elements during 12/15/2015–

01/07/2016, d) Wind rose for the day with the highest concentration of F4 (industrial Pb) (03/06/2016), e)

Correlation matrix of trace elements during 03/06/2016.

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Fig. SD4. Variability of species of Non–ferrous metal smelting (Cu) factor.

Fig. SD5. Variability of species of Industrial (Pb) factor.

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Fig. SD6. a) Wind rose for the first period with higher concentration of F5 (secondary PM) (01/07/2016–

02/06/2016), b) Polar plot of K during the first peak period based on daily data, c) Correlation matrix of trace

elements during the first peak period, d) Wind rose for the second period with higher concentration of F5

(03/05/2016–03/13/2016), e) Polar plot of SO42– during the second peak period based on daily data, f)

Correlation matrix of trace elements during the second peak period.

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