Redox characteristics of size segregated PM from different public transport microenvironments in Hong Kong

Nirmal Kumar Gali1, Sabrina Yanan Jiang1, Fenhuan Yang1, Li Sun1, Zhi Ning1,2,*

1 School of Energy and Environment, City University of Hong Kong, Hong Kong

2 Guy Carpenter Climate Change Centre, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region

Supplementary information

Figure S1: Gravimetrical mass concentration difference for Fine PM and coarse PM of MTR routes UG, AG and BUS used for different analysis. The data shown is average PM mass concentration Mean±SD of three replica filters used for Metals and Toxicity analysis, Ion and OCEC quantification (n=3) at respective routes.

Table S1: Summary of associations between metals of fine PM and endogenous ROS. (a) per m3 of air volume, (b) per microgram PM. “++, +” and “--,-“ represents positive and negative associations, respectively, where N=3; “++” is a positive correlation where R > 0.5, and “—“ are negative correlation when R < -0.5. No significant correlations were observed at n=3.

(a) R value on volume basis / (b) R value per mass basis
UG / AG / BUS / UG / AG / BUS
Al / - / ++ / -- / + / ++ / --
Ba / -- / ++ / - / ++ / ++ / -
Cd / -- / + / -- / -- / - / --
Cr / - / ++ / -- / - / ++ / +
Cu / -- / ++ / -- / + / ++ / +
Mn / -- / ++ / -- / + / + / -
Mo / -- / ++ / ++ / + / -- / ++
Ni / ++ / ++ / ++ / ++ / + / ++
Pb / - / + / -- / -- / -- / --
Sb / -- / ++ / -- / -- / - / --
V / ++ / - / ++ / + / - / ++
Zn / -- / + / - / -- / - / ++
Ca / - / ++ / -- / -- / -- / +
Fe / - / ++ / - / -- / ++ / -
Mg / + / ++ / -- / + / -- / ++
Na / ++ / ++ / ++ / ++ / - / ++
K / - / + / -- / -- / - / --

Table S1 shows the overview of correlation between metals and PM-induced ROS activity for the samples collected at UG, AG and BUS routes individually. On air volume basis as shown in Table S1a, the degree of correlation for endo-ROS varied largely with different metals, where Ni in fine PM registered significant and positive association with R values >0.73 (N=3) for all 3 routes. In addition, V (R>0.62, N=3) and Na (R>0.69, N=3) positive correlations were predominantly seen in UG and BUS routes. The Mo (0.70R<0.78, N=3) was found significantly correlating with endo-ROS at AG and BUS routes. All other metals including Al, Ba, Cr, Cu, Mn, Ca and Fe were predominantly (R>0.85, N=3) seen positively associated with endo-ROS of AG route, with Mg and Sb showing weak associations (0.50R<0.58, N=3). Lack of data points for these metals in coarse PM inhibited the study of their roles in the generation of cellular ROS, while earlier reports have shown their high correlations with PM-induced ROS (Kam et al., 2011b). However, the validity of these correlations appears to depend on the variation in sampling routes, source and bioavailability. It is noted that, exo-ROS is strongly associated with Al, Cd, Cr, Mn, Pb, Zn and Fe in fine PM UG site, which is not the case with endo-ROS. Similarly, Mo is significantly correlated with endo-ROS, but not with exo-ROS.

On PM mass basis (Table S1b), the correlation trends were different that the metals are equally distributed among 3 routes, compared to volume analysis where AG route alone predominated. However, Ni (R>0.82, N=3), V (0.50>R<0.78, N=3), and Na (R>0.66, n=3) remained predominant for respective routes. All other metals were seen shared between routes, where Ba, Cu and Mn accounted for R>0.53, N=3 with UG and AG routes, while Mo (R=0.89, N=3), Zn (R=0.71, N=3) and Mg (R=0.92, N=3) were predominant only at BUS route, and Al (R=0.93, N=3), Cr (R=0.82, N=3) and Fe (R=0.98, N=3) only at AG route. The strong correlation between Al, Cr, Mn, Fe, Ni and Mo suggests that these elements, which may be components of stainless steel used on MTR tracks, may share a common source (Kam et al., 2011b). It also has to be noted that strong associations of Ni, V (heavy oil combustion), Zn (roadway dust) etc (Cheng et al., 2015; Kumar et al., 2013) at respective sites suggests identification of true surrogates to cell toxicity, however caution is required when identifying single surrogates for PM oxidative potential.

Table S2: Quantitative estimation of EC and OC in PM samples. Measured amounts of EC and OC (a) per m3 of air volume, and (b) per milligram of PM mass. (c) Ratio of OC to EC. Data presented are Mean ± SD of three independent determinations.

Route / PM / (a) per volume / (b) per mass / (c) OC/EC
EC
(μg/m3) / OC
(μg/m3) / EC
(μg/mg) / OC
(μg/mg)
UG / Fine / 2.46
±0.62 / 4.14
±0.71 / 82.57
±32.41 / 139.64
±55.41 / 1.705
±0.17
Coarse / 0.53
±0.40 / 2.27
±0.93 / 36.83
±20.49 / 173.60
±81.78 / 5.060
±1.55
AG / Fine / 5.22
±1.51 / 9.08
±2.83 / 76.82
±18.16 / 134.21
±36.00 / 1.748
±0.25
Coarse / 1.74
±0.29 / 3.83
±0.64 / 67.47
±19.93 / 148.18
±40.50 / 2.205
±0.07
BUS / Fine / 4.44
±1.64 / 5.51
±1.96 / 104.60
±7.99 / 130.10
±6.03 / 1.252
±0.15
Coarse / 0.69
±0.18 / 2.53
±0.39 / 56.15
±5.93 / 208.40
±11.18 / 3.742
±0.46

Figure S2: Air Quality at Sha Tin as measured based on concentrations of NO2, O3, PM10 and PM2.5.

Figure S3: Air Quality at Sham Shui Po as measured based on concentrations of NO2, O3, PM10 and PM2.5.

The monthly average pollutants concentrations were acquired from the general air monitoring station of Environmental Protection Department, Hong Kong (HKEPD, 2015). The sampling in the ambient sites presented in the manuscript was during November 2012 to May 2013, while the commute sampling was carried out between October 2013 and April 2014. However, Figure S2 and S3 from the Sha Tin and Sham Shui Po sites, representing residential and urban sites, respectively, showed very similar trend and pattern in different years. In other words, there is no major change in the PM and co-pollutants characteristics during the study periods.

References:

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