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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: [email protected].
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.
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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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