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 July 2014 Available online 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 (http://creativecommons.org/licenses/by-nc-nd/3.0/) Keywords: PM NO2 Asia Socioeconomic status Exposure assessment * Corresponding author E-mail addresses: hind.sbihi@ubc.ca, hind.sbihi@gmail.com (H Sbihi) http://dx.doi.org/10.1016/j.atmosenv.2014.07.011 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 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 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 (1 standard deviation) increase in AC use, the personal PM2.5 and PM10 578 S Mehta et al / Atmospheric Environment 95 (2014) 571e580 Table 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 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 (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 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 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 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 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 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; 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 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 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biomass use, and air pollution in Accra, Ghana PNAS 108, 11028e11033 http://dx.doi.org/10.1073/pnas.1019183108  ... 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... 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... 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