DSpace at VNU: Effect of poverty on the relationship between personal exposures and ambient concentrations of air pollutants in Ho Chi Minh City

Atmospheric Environment 95 (2014) 571e580

Contents lists available at ScienceDirect

Atmospheric Environment
journal homepage: www.elsevier.com/locate/atmosenv

Effect of poverty on the relationship between personal exposures and
ambient concentrations of air pollutants in Ho Chi Minh City
Sumi Mehta a, Hind Sbihi b, *, Tuan Nguyen Dinh c, Dan Vu Xuan d, Loan Le Thi Thanh e,
Canh Truong Thanh f, Giang Le Truong g, Aaron Cohen a, Michael Brauer b
a

Health Effects Institute, Boston, MA, USA
School of Population and Public Health, University of British Columbia, 2206 East Mall, Vancouver, BC V6T 1Z2, Canada
Ho Chi Minh City Environmental Protection Agency (HEPA), Institute for Environment and Resources (IER), The National University of Ho Chi Minh City,
Viet Nam
d
Center for Occupational and Environmental Health, Viet Nam
e
Ho Chi Minh City Bureau of Statistics, Viet Nam
f
Ho Chi Minh City University of Science, Viet Nam
g
Department of Public Health, Viet Nam
b
c

h i g h l i g h t s
 We examined the pollutant exposureepoverty relationship in Ho Chi Minh, Vietnam.
 Personal exposures to particles and NO2 were higher amongst the poor.

 Ambient levels poorly reflect personal exposures, in particular for poor residents.
 In addition to socioeconomic status, behavioral factors determined exposure levels.

a r t i c l e i n f o

a b s t r a c t

Article history:
Received 15 April 2014
Received in revised form
30 June 2014
Accepted 3 July 2014
Available online 3 July 2014

Socioeconomic factors often affect the distribution of exposure to air pollution. The relationships between health, air pollution, and poverty potentially have important public health and policy implications,
especially in areas of Asia where air pollution levels are high and income disparity is large. The objective
of the study was to characterize the levels, determinants of exposure, and relationships between children
personal exposures and ambient concentrations of multiple air pollutants amongst different socioeconomic segments of the population of Ho Chi Minh City, Vietnam. Using repeated (N ¼ 9) measures
personal exposure monitoring and determinants of exposure modeling, we compared daily average
PM2.5, PM10, PM2.5 absorbance and NO2 concentrations measured at ambient monitoring sites to measures of personal exposures for (N ¼ 64) caregivers of young children from high and low socioeconomic
groups in two districts (urban and peri-urban), across two seasons. Personal exposures for both PM sizes
were significantly higher among the poor compared to non-poor participants in each district. Absolute
levels of personal exposures were under-represented by ambient monitors with median individual
longitudinal correlations between personal exposures and ambient concentrations of 0.4 for NO2, 0.6 for
PM2.5 and PM10 and 0.7 for absorbance. Exposures of the non-poor were more highly correlated with
ambient concentrations for both PM size fractions and absorbance while those for NO2 were not
significantly affected by socioeconomic position. Determinants of exposure modeling indicated the
importance of ventilation quality, time spent in the kitchen, air conditioner use and season as important
determinant of exposure that are not fully captured by the differences in socioeconomic position. Our
results underscore the need to evaluate how socioeconomic position affects exposure to air pollution.

Here, differential exposure to major sources of pollution, further influenced by characteristics of Ho Chi
Minh City's rapidly urbanizing landscape, resulted in systematically higher PM exposures among the
poor.
© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND
license ( />
Keywords:
PM
NO2
Asia
Socioeconomic status
Exposure assessment

* Corresponding author.
E-mail addresses: , (H. Sbihi).
/>1352-2310/© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under t
personal concentrations in both districts.

3.4. Exposure factors: determinants of personal concentrations

Fig. 3. Box plots of PM2.5 and NO2 individual longitudinal correlations between personal and ambient measurements.

Given the high correlation between the two fixed-site PM
measurements, ambient PM was averaged between the 2 sites for
all subsequent modeling. In examining whether the association
between ambient and personal concentrations was modified by SES
or other activities and/or time spent in different microenvironments and/or activities, we built determinants of personal
PM concentrations models (Table 7).
The determinants of exposure modeling indicated that SES and
the time in which air conditioning (AC) was used both predicted the
personal exposure for PM2.5 and PM10 in the expected direction (i.e.

stronger association for non-poor compared with poor participants
and lower personal concentration with increased time of AC usage).
For a standard deviation increase in ambient concentration of PM2.5
(21 mg/m3) and PM10 (38 mg/m3), the personal concentration
increased by 18.5 and 57 mg/m3 respectively. For a 120 min (1
standard deviation) increase in AC use, the personal PM2.5 and PM10


578

S. Mehta et al. / Atmospheric Environment 95 (2014) 571e580

Table 5
Summary estimates (mean and median) of individual longitudinal correlation between ambient (mean levels between two fixed site monitors for PM, and nearest monitor for
NO2) and personal pollutants levels by SES and by season.

Non-poor
Poor
Dry
Rainy
Overall

a
b

Mean
Median
Mean
Median
Mean

Median
Mean
Median
Mean
Median
IQR

Averagea,b PM2.5

Average PM10

Average Abs PM2.5

Nearest station NO2

0.62a
0.75
0.37a
0.44
0.45b
0.50
0.54b
0.62
0.50
0.59
0.54

0.57a
0.68
0.43a

0.58
0.49
0.60
0.50
0.60
0.50
0.60
0.58

0.53
0.71
0.56
0.66
0.51
0.66
0.58
0.71
0.55
0.69
0.49

0.36
0.40
0.35
0.50
0.37
0.50
0.34
0.40
0.36

0.43
0.55

Statistically different by SES.
Statistically different by season.

concentration decreased by 1.4 and 5.5 mg/m3 respectively. In
addition, smoking was a significant predictor of PM2.5 exposures,
while distance to the nearest road (as provided by the initial
household questionnaire) was positively associated with the personal concentration of PM10, but not PM2.5 nor absorbance. Season,
a categorical variable relatively balanced among the two strata of
SES (31 poor subjects provided samples in each of the rainy and dry
season vs. 35 and 33 for the non-poor study participants), had a
different effect on the personal level of PM2.5 absorbance compared
with the personal levels of NO2. For the latter, being in the rainy
season increased the personal concentration of NO2 by 1.8 ppb,
implying a stronger association between ambient and personal
concentration. In contrast, the personal PM2.5 absorbance
decreased by 0.62 mÀ1 Â 10À5, for the rainy vs. dry season leading to
a weaker outdoor to personal association in the rainy season
compared with the dry season.
For NO2, both in District 2 and BT, questionnaire variables
explained more variability in personal concentration than the socioeconomic position of the study participants. The quality of the
ventilation in the kitchen was an important factor in the personal
concentration as every unit drop in ventilation quality (e.g. from
moderate to bad) was associated with 2.5 and 2.3 ppb decrease in
the personal concentration in D2 and BT respectively which corresponds approximately to a five percentile downshift.
Regarding model fit, the determinants of personal PM concentration for both PM size fractions explained less between-subject
variability compared with absorbance and NO2. It is important to
note however, that direct comparison of goodness of fit for these

models is not feasible since the main predictors differed as a
function of the pollutant that was considered.

regardless of district of residence. By comparing more precise estimates of individual personal exposure with estimates based on
the ambient monitoring stations, we were able to explore systematic daily differences in exposure e major sources and levels e
across socioeconomic position.
We found that measured personal exposure was not well represented by ambient concentration measurements in most circumstances. This is because exposure while partly reflected by
ambient concentration measurements is also influenced by
neighborhood “hot spots” as well as micro-environmental levels
experienced by individuals according to their personal behaviors.
We compared measurements of individual personal exposure with
estimates based on concentrations measured at ambient monitoring stations and found that there were systematic differences in
these relationships across socioeconomic position and seasons for
both PM2.5 and PM10. Measured personal exposures of the poor
were less correlated to those estimated from ambient monitors.
In addition, ambient monitoring substantially underestimated
personal exposures for all measured pollutants in Ho Chi Minh City,
with a significantly higher underestimation among the poor for fine
PM. Daily mean concentrations for PM measured at the fixed sites
during the same time period were lower than the personal measurements, with BT district showing higher levels compared to
those measured in District 2 (95.2 vs. 77.8 mg/m3 for PM10 and 50.1
vs.39.2 mg/m3 for PM2.5). Similar results were apparent for NO2 with
higher personal measurements compared with those from fixed
sites, with significantly higher concentrations in BT district
compared with District 2, and significant differences between poor
and non-poor participants only in District 2.
Thus, localized sources appeared to contribute to exposure error
arising from the use of ambient monitoring site data for health
effects assessments, Further, the relative contribution of different
sources of exposure differed by socioeconomic position.

A wide distribution of daily personal exposures to PM10
and PM2.5 were measured, with average exposures of 103.4 and
64.6 mg/m3 respectively, along with mean NO2 personal exposure of
16.2 ppb. This is consistent with the distribution of ambient air

4. Discussion
Using monitoring and modeling based approaches, we evaluated whether poorer children in Ho Chi Minh City systematically
experienced higher exposures to air pollution per level of ambient
air pollution on any given day compared to non-poor children,

Table 6
Effect of district and SES in personal/ambient concentrations repeated measures models.
Personal measurements
PM2.5 model

Ambient
SES (non-poor)
District

PM10 model

NO2 model in BT

NO2 model in D2

b

95% CI

b

95% CI

b

95% CI

b

95% CI

0.66
8.2
3.4

0.5; 0.8
0.4; 16
À15.4; 22.3

0.57
11.4
2

0.4; 0.7
0.9; 22
À7.6; 11.8

0.46
À1.03

0.4; 0.6
À3.7; 1.7

0.38
À0.96

0.3; 0.5
À3.7; 1.8


S. Mehta et al. / Atmospheric Environment 95 (2014) 571e580

579

Table 7
Final explanatory models showing significant variables affecting the association between personal and ambient NO2, PM2.5 and PM10 concentrations and absorbance.
PM10

PM2.5

b
Ambient
SES (non-poor)
Season (Dry)
Time spent in kitchen
Distance to road
Distance to nearest monitor
Smoking (self)
Vent. quality (kitchen)
Use of AC (min/day)

0.6
9.5

2.5
À0.01

Abs. PM2.5

95% CI

b

95% CI

0.4; 0.7
À0.3; 19

0.67
22.4

0.19; 1.14
1.3; 43.5

À2.3
0.01

À4.2; À0.3
0; 0.01

b

NO2 in D2
95% CI

0.44
À0.62
0.07

0.27; 0.6
À1.2; À0.06
0.01; 0.12

b

NO2 in BT
95% CI

0.39

0.2; 0.5

b

95% CI
0.37

0.2; 0.5

1.8

À0.29

0.18; 3.4
À0.5; À0.09

À0.01

À0.02; À0.01

À0.01

À0.01; 0

À2.5
À0.01

À4.1; À0.9
À0.01; À0.01

À2.3

À4.2; À0.4

0.7; 4.2
À0.03; À0.003

À0.04

À0.07; À0.02

AC: Air conditioning.

pollution levels in HCMC, which are generally higher than those
reported in developed countries, but lower than levels observed in
other Asian mega-cities. Personal concentrations for both PM sizes
were significantly higher among those classified as poor compared
to participants who were classified as non-poor. Zhou and colleagues also demonstrated an SES gradient in PM levels in Accra,
Ghana (lowest PM in the high-SES neighborhood, and highest in
two of the low SES slums with geometric means reaching 71 and
131 mg/m3 for fine and coarse PM) (Zhou et al., 2011).
Median longitudinal correlations between personal and
ambient monitors were 0.4 for NO2, 0.6 for PM2.5 and PM10 and 0.7
for absorbance. These correlations were somewhat lower than
those observed in similar studies (Brunekreef et al., 2005; Janssen
et al., 1998, 2005; Noullett et al., 2006; Wallace, 2000) conducted
in developed countries (median longitudinal correlation (#
days) ¼ 0.74 (4e8), 0.73 (10), 0.49 (2days for 23 weeks), for PM2.5,
PM10, Absorbance, and NO2, respectively).
Along with the socioeconomic gradient found in exposure to
PM in HCMC, the exposures of the non-poor were more highly
correlated with ambient measurements for both PM size fractions
while those found for NO2 were not significantly affected by SES.
This suggests that different PM sources may be influencing the
exposures of the poor and non-poor. Our analysis of the household
characteristics and time activity patterns collected along with the
personal sampling campaign shed some light on these sources as
well as factors that would alter the relationship between fixed site
and personal measurements. For instance, the quality of the
ventilation in the kitchen was significantly different between the
two SES strata, with the poor having worse ventilation quality

than non-poor study participants. This modifier was among the
main predictors of the model for personal exposures. From the
TAP diaries, differences in personal factors between the poor and
non-poor were more predominant than time spent in different
micro-environments as we observed statistically significant differences between poor and non-poor HCMC residents participating in the study: the poor smoked and used fans more, while
the non-poor were more frequent users of AC. In order, to disentangle the roles of all the factors captured in the questionnaires
and the daily diaries from the role played by SES, we examined the
association between personal and ambient in two steps: first
without including SES and offering all significant predictors in the
bivariate analysis; second forcing SES in the same models. Should
the TAP and questionnaire variables be explained by the socioeconomic position, the multicollinearity would lead to only the
stronger predictors remaining in the final model of the determinants of personal exposures.
Overall, the models for the determinants of personal exposure to
NO2, PM10, PM2.5 and absorbance indicated ventilation quality and
time spent in the kitchen, AC use and season as important factors
that were not fully captured by SES differences. These results

indicate that epidemiologic analysis examining the effects of air
pollution on health may be biased if surrogates of SES are not
included. Furthermore, more detailed information capturing the
specificities of developing countries (e.g. ventilation quality and AC
use) would reduce the potential for different degrees of exposure
misclassification that may be related to SES. Other influential indoor air quality determinants, such as type of cooking devices used
may have provided further insight in the SES gradient found in the
examined pollutants; for although nearly all households (92%) used
LPG as their cooking fuel, kerosene use was elevated in the poor
(12.5%) compared to the non-poor (3%) households.
Results of this study also aid in the interpretation of the companion hospital study, where analyses were not able to identify
differential effects by socioeconomic position (Mehta et al., 2013).
In the hospital study, a single daily measurement of pollution was

assigned to all children for a particular day. As such, daily differences in individual exposures across districts or socioeconomic
groups could not be adequately assessed. This study lends further
support to the hypothesis that poorer children in Ho Chi Minh City
systematically experience higher exposures to air pollution per unit
of reported ambient air quality on any given day compared to nonpoor children, regardless of district of residence. If the exposures of
the poor are less well correlated with measurements made at the
fixed sites used in epidemiologic analyses, there will be more
exposure misclassification among the poor. This would be expected
to result in a decreased ability to assess the true association between short-term air pollution exposure and adverse health outcomes among the poor, and will limit the ability to assess
differences in risk by socioeconomic position. Our investigation is
based on the premise that the siting of the two ambient monitors is
representative of average ambient concentrations within the surrounding area where participants resided. We examined and
confirmed that (1) residents were living at similar distances to the
nearest major road (245 m in BT vs. 267 m in District 2 based on
study technicians report), and (2) that road density was not
significantly different around households and the corresponding
monitor in each district. However, we have no data to examine the
distribution of industries across the two districts, although most
industries are small-scales operations and located mainly within
residential areas.
Differential exposure to major sources of pollution, further
influenced by characteristics of Ho Chi Minh City's rapidly urbanizing landscape, resulted in systematically higher exposures among
the poor. Our experience documents potential for differential
misclassification of air pollution exposure by SES when using
ambient pollution monitors located in areas that differ in the
relative contribution of different sources of pollution and other
aspects of the urban environment correlated with SES. These results underscore the need to carefully evaluate how socioeconomic
position may influence exposure to air pollution.

580

S. Mehta et al. / Atmospheric Environment 95 (2014) 571e580

Acknowledgments
The authors would like to acknowledge the contributions of
HEPA field and lab staff, the International Scientific Oversight
Committee, and Timothy McAuley as well as the Bureau of Statistics
field staff.
This project is supported with funds from the Health Effects
Institute and the Poverty Reduction Cooperation Fund of the Asian
Development Bank (Technical Assistance TA 4714-VIE), as well as
in-kind support from the Government of Vietnam.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.atmosenv.2014.07.011.
References
Allen, R., Box, M., Liu, L.-J.S., Larson, T.V., 2001. A cost-effective weighing chamber
for particulate matter filters. J. Air Waste Manag. Assoc. 51, 1650e1653. http://
dx.doi.org/10.1080/10473289.2001.10464392.
Brunekreef, B., Janssen, N.A.H., de Hartog, J.J., Oldenwening, M., Meliefste, K.,
Hoek, G., et al., 2005. Personal, indoor, and outdoor exposures to PM2.5 and its
components for groups of cardiovascular patients in Amsterdam and Helsinki.
Res. Rep. Health Eff. Inst., 1e70 discussion 71e79.
Finkelstein, M.M., Jerrett, M., Sears, M.R., 2005. Environmental inequality and circulatory disease mortality gradients. J. Epidemiol. Community Health 59,
481e487. />HEI International Scientific, 2010. Outdoor Air Pollution and Health in the Developing Countries of Asia: a Comprehensive Review. Special report 18.
ISO 9835, 1993. Ambient Air e Determination of a Black Smoke Index.
Janssen, N.A., Hoek, G., Brunekreef, B., Harssema, H., Mensink, I., Zuidhof, A., 1998.
Personal sampling of particles in adults: relation among personal, indoor, and
outdoor air concentrations. Am. J. Epidemiol. 147, 537e547.

Janssen, N.A.H., Lanki, T., Hoek, G., Vallius, M., de Hartog, J.J., Van Grieken, R., et al.,
2005. Associations between ambient, personal, and indoor exposure to fine
particulate matter constituents in Dutch and Finnish panels of cardiovascular
patients. Occup. Environ. Med. 62, 868e877. />oem.2004.016618.

Laurent, O., Bard, D., Filleul, L., Segala, C., 2007. Effect of socioeconomic status on the
relationship between atmospheric pollution and mortality. J. Epidemiol. Community Health 61, 665e675. />Le, T.G., Ngo, L., Mehta, S., Do, V.D., Thach, T.Q., Vu, X.D., et al., 2012. Effects of shortterm exposure to air pollution on hospital admissions of young children for
acute lower respiratory infections in Ho Chi Minh City, Vietnam. Res. Rep.
Health Eff. Inst., 5e72 discussion 73e83.
Lim, S.S., Vos, T., Flaxman, A.D., Danaei, G., Shibuya, K., Adair-Rohani, H., et al., 2012.
A comparative risk assessment of burden of disease and injury attributable to
67 risk factors and risk factor clusters in 21 regions, 1990e2010: a systematic
analysis for the Global Burden of Disease Study 2010. Lancet 380, 2224e2260.
/>Mehta, S., Ngo, L.H., Dzung, D.V., Cohen, A., Thach, T.Q., Dan, V.X., et al., 2013. Air
pollution and admissions for acute lower respiratory infections in young children of Ho Chi Minh City. Air Qual. Atmos. Health 6 (1), 167e179. http://
dx.doi.org/10.1007/s11869-011-0158-z.
Noullett, M., Jackson, P.L., Brauer, M., 2006. Winter measurements of children's
personal exposure and ambient fine particle mass, sulphate and light absorbing
components in a northern community. Atmos. Environ. 40, 1971e1990. http://
dx.doi.org/10.1016/j.atmosenv.2005.11.038.
Sarnat, J.A., Koutrakis, P., Suh, H.H., 2000. Assessing the relationship between personal particulate and gaseous exposures of senior citizens living in Baltimore,
MD. J. Air Waste Manag. Assoc. 50, 1184e1198.
Smith, K.R., Samet, J.M., Romieu, I., Bruce, N., 2000. Indoor air pollution in developing countries and acute lower respiratory infections in children. Thorax 55,
518e532.
Wallace, L., 2000. Correlations of personal exposure to particles with outdoor air
measurements: a review of recent studies. Aerosol Sci. Technol. 32, 15e25.
/>Wang, H., Dwyer-Lindgren, L., Lofgren, K.T., Rajaratnam, J.K., Marcus, J.R., LevinRector, A., et al., 2012. Age-specific and sex-specific mortality in 187 countries,
1970e2010: a systematic analysis for the Global Burden of Disease Study 2010.
Lancet 380, 2071e2094. />Wong, C.-M., Vichit-Vadakan, N., Kan, H., Qian, Z., 2008. Public health and air
pollution in Asia (PAPA): a multicity study of short-term effects of air pollution

on mortality. Environ. Health Perspect. 116, 1195e1202. />10.1289/ehp.11257.
Wong, C.M., Vichit-Vadakan, N., Vajanapoom, N., Ostro, B., Thach, T.Q., Chau, P.Y.K.,
et al., 2010. Part 5. Public health and air pollution in Asia (PAPA): a combined
analysis of four studies of air pollution and mortality. Res. Rep. Health Eff. Inst.,
377e418.
Zhou, Z., Dionisio, K.L., Arku, R.E., Quaye, A., Hughes, A.F., Vallarino, J., et al., 2011.
Household and community poverty, biomass use, and air pollution in Accra,
Ghana. PNAS 108, 11028e11033. />