Article pubs.acs.org/est
Differential Distributed Lag Patterns of Source-Specific Particulate Matter on Respiratory Emergency Hospitalizations Vivian C. Pun,§ Linwei Tian,*,∥ Ignatius T.S. Yu,† Marianthi-Anna Kioumourtzoglou,‡ and Hong Qiu† †
Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, SAR China Department of Environmental Health, Harvard School of Public Health, Harvard University, Boston, Massachusetts 02115, United States
‡
S Supporting Information *
ABSTRACT: While different emission sources and formation processes generate mixtures of particulate matter (PM) with different physicochemical compositions that may differentially affect PM toxicity, evidence of associations between PM sources and respiratory events is scarce. We estimated PM10 sources contributed from 19 chemical constituents by positive matrix factorization, and examined association of short-term sources exposure with emergency respiratory hospitalizations using generalized additive models for single- and distributed lag periods. PM10 contributions from eight sources were identified. Respiratory risks over a consecutive 6-day exposure period were the highest for vehicle exhaust [2.01%; 95% confidence interval (CI): 1.04, 2.99], followed by secondary sulfate (1.59%; 95% CI: 0.82, 2.37). Vehicle exhaust, regional combustion, and secondary nitrate were significantly associated with 0.93%−2.04% increase in respiratory hospitalizations at cumulative lag2−5; significant associations of aged sea salt (1.2%; 95% CI: 0.63, 1.78) and soil/road dust (0.42%; 95% CI: 0.03, 0.82) were at lag0−1. Some effect estimates were no longer significant in two-pollutant models adjusting for PM10; however, a similar temporal pattern of associations remains. Differential lag associations of respiratory hospitalizations with PM10 sources were indicated, which may reflect the different particle size fractions that sources tend to emit. Findings may have potential biological and policy implications.
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INTRODUCTION While it has been established that short-term exposure to ambient particulate matter (PM) pollution is positively associated with respiratory events, the debate about which of the specific PM characteristics are most toxic to health remains. Significant heterogeneity in risk estimates suggests that the aspect of PM most harmful to health may not be best quantified by mass concentrations of particles with aerodynamic diameter ≤10 μm (PM10) and those with ≤2.5 μm (PM2.5) alone.1 Since PM sources and formation processes generate mixtures of air pollutants with different physicochemical compositions, this would be reflected by increased variability in PM toxicity. This hypothesis is supported by toxicological evidence suggesting that PM-induced biologic effects depended on the zone of origin (e.g., industrial zone).2,3 Quantitatively estimating source-specific health risks is challenging. Rather than observing PM source contributions directly, PM exposures are measured in samples of ambient air that reflect dynamic mixtures of source contributions.4 Earlier studies addressed this issue partially using surrogate pollutants, road density, or in-traffic exposure to model traffic-related pollution and estimate its pulmonary toxicity.5,6 For nearly 40 years, atmospheric scientists have been using source apportionment model, a technique that statistically apportions observed pollutant concentrations to its contributing sources, to help © 2015 American Chemical Society
guide its air pollution monitoring and emission control polices. Not until the early 2000s was effort made to apply such technique in health analysis. Since then, a growing number of epidemiologic studies, mostly conducted in the Western atmosphere, began to provide evidence for the role of different PM sources on health outcomes using source apportionment modeling. In a review, Stanek et al.7 pointed out that while there was indication of pulmonary associations for secondary sulfate, soil/road dust, and traffic, heterogeneity in findings remained. This might be explained partially by the limited number of studies available on the topic, and by the spatiotemporal variations in pollution emission sources and in population susceptibility. Further investigation into the health associations of source-apportioned PM mass, particularly under Asian atmosphere, is warranted in order to improve our understanding of the source-specific health risks. Ambient particulate concentrations in Hong Kong often exceed the recommended World Health Organization’s Air Quality guidelines.8 Pun et al.9 has linked PM10 representing vehicle exhaust, secondary nitrate, and aged sea salt with Received: Revised: Accepted: Published: 3830
October 17, 2014 January 29, 2015 February 4, 2015 February 4, 2015 DOI: 10.1021/es505030u Environ. Sci. Technol. 2015, 49, 3830−3838
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Environmental Science & Technology
largely resides in a particular source. Detailed PMF procedures have been reported elsewhere.11 Subsequent to source apportionment analysis, we used a method of centering and averaging, which has been described in detail in our previous studies, to remove the station-specific influence on the resulting PMF-resolved PM 10 source contributions.9,16 Given that Hong Kong is a small territory and the temporal fluctuations of levels of PM10 constituents and consequently sources across monitoring stations were fairly uniform (temporal correlations between monitors across pollutants >0.70), combining data from multiple monitoring stations to compute the territory-wide mean concentrations has been considered reasonable to represent Hong Kong’s population-averaged exposure levels. The resultant PM10 sources time-series contained nonmissing territory-wide mean contributions of PM10 sources for 2041 study days, which is equivalent to complete data for five consecutive days a week. No notable difference was observed between nonmissing sampling days and missing data in terms of respiratory admissions, PM10 mass, and meteorological conditions. Previous Hong Kong studies have shown that the risk estimates of PM10 constituents and sources were insensitive to either regression models in which no data imputation was used or models in which imputation of missing sampling days was applied.9,16 Therefore, we imputed source contributions for days without samples from any monitoring stations (881 days) by linear regression using the na.approx function in the R zoo package to obtain the final complete time-series in order to examine the temporal and distributed lag structure of the association between PM sources and respiratory hospitalization. All pollutant concentrations were expressed in μg/m3. Risk of respiratory hospitalization associated with PM10 sources was estimated using generalized additive models with log link, Poisson-distributed errors, and two autoregressive terms.17 We adjusted for time-varying confounders in the models, with smoothing splines with 8 degrees of freedom (df) per year for time trends and seasonality, 6 df for current day temperature and previous 3-day moving average, and 3 df for current day relative humidity and previous 3-day moving average selected a priori.18,19 We also controlled for day of week, public holidays, and influenza epidemics. We examined the distribution of associations of respiratory hospitalizations with PM10 sources over time (lag-effect window) using three separate temporal lag models. First, we fitted single-lag models (i.e., one lag term per model) to examine associations with exposure to each PM10 source on the same day (lag0) and up to 5 days (lag5) prior to hospitalization, the lag window shown to be the most relevant exposure period.20 Second, we applied constrained distributed lag models (DLM) to investigate the shape of distributed effect of PM10 sources on hospitalizations over time, by simultaneously including exposure on the same day and prior 5 days, and constraining the lag associations to follow a priori third-degree polynomial shape.21 Such application of constraint gives enough flexibility to estimate a biologically plausible lag structure, and allows better control for multicollinearity between lags. Finally, we employed unconstrained DLM to obtain unbiased estimate of the overall hospitalization risk of PM10 source for three a priori cumulative lag intervals, which are lag0−1, lag2−5, and lag0−5.21−23 These unconstrained DLMs were carried out by introducing multiple lag exposures concurrently (e.g., lag0 and lag1 for lag0−1 analysis) into the
increased hospitalizations for ischemic heart diseases in Hong Kong. In the present study, we estimated association of shortterm exposure to PM10 sources with daily respiratory emergency hospital admissions. We also took advantage of the unique nature of our composite daily PM10 source data to examine temporal lag structure between particulate sources and health outcomes, which has been difficult in past studies.
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MATERIALS AND METHODS Hospitalization Data. Time-series health analyses were conducted using daily counts of emergency hospital admissions into twenty-six publicly funded, regulated by the Hong Kong Hospital Authority, hospitals across the territory between 2001 and 2008. The principle diagnosis on discharge was coded according to the ninth revision of the International Classification of Diseases (ICD-9). Hospitalizations for total respiratory diseases (ICD-9:460−519) were extracted to construct the time series. To be consistent with previous studies,9,10 influenza hospitalizations (ICD-9:487) were removed from the respiratory disease category. Pollution and Meteorological Data. We obtained speciation data derived from 24-h PM10 filter samples collected between 1 January 2001 and 31 December 2008 at six general air quality monitoring stations maintained by the Hong Kong Environmental Protection Department. These stations served to capture the air quality that the general population was exposed to on a regular basis. Details on the sampling frequency and analytical methods were published elsewhere.9,11 Briefly, each station operated at a distinct sampling schedule of on average every-sixth-day (e.g., Monitor A measured data on Jan 1st, 7th, and 13th of 2002 and so on; Monitor B measured data on Jan 3rd, 8th, and 14th of 2002 and so on). Collectively, the stations covered speciation measurements for 2041 unique study days (∼70% of all study days). The measurements were subsequently used to apportion PM10 mass to various source types. We also obtained daily mean temperature, relative humidity from the Hong Kong Observatory for the same study period. See Supporting Information (SI) Figure S1 in the online supplement for the location of hospitals and monitoring stations included in the study. Design and Statistical Analysis. We used the U.S. Environmental Protection Agency’s positive matrix factorization (PMF) version 3.0 to identify a set of factors, interpreted as emission sources, and to estimate source-specific contributions to PM10 mass.12,13 We conducted source apportionment using station-specific measurements of elemental carbon, organic matter, nitrate, sulfate, ammonium ion, chloride ion, sodium ion, potassium ion, aluminum, arsenic, calcium, cadmium, iron, magnesium, manganese, nickel, lead, vanadium, and zinc. Hong Kong is a small territory with a linear dimension of 40 × 30 km2, and the source profiles at different monitoring stations are presumed to be similar. Thus, speciation measurements from all six monitoring stations were considered as independent data points for the same set of emission sources, and were simultaneously included in a single source apportionment model. Concentration of PM10 mass were also imported into the model to aid estimation of factor contribution to total PM10 mass.14,15 Six factor solutions were initially tested, and we increased the number until the results were no longer interpretable. Source profiles of different factor solutions were evaluated, and source categories were mainly determined by identifying their chemical “tracer” species, which is typically the constituent that exclusively or 3831
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Table 1. Descriptive Statistics for PM10 Sources, Meteorological Factors and Number of Emergency Hospital Admissions in Hong Kong, 2001−2008a daily mean ± SD
variables emergency hospital admissions (counts) total respiratory meteorological conditions temperature (°C) relative humidity (%) pollutant concentration (μg/m3) PM10 vehicle exhaust soil/road dust regional combustion residual oil fresh sea salt aged sea salt secondary nitrate secondary sulfate a
IQR
percent of PM10
226 ± 48
60
NA
23.5 ± 5.0 78.1 ± 10.2
8.1 11.6
NA NA
± ± ± ± ± ± ± ± ±
44.2 4.6 6.7 10.8 2.2 2.0 5.6 9.0 15.9
100.0 14.3 12.8 13.0 4.4 3.5 12.7 15.2 24.4
55.7 8.0 7.1 7.3 2.5 2.0 7.1 8.5 13.6
31.0 3.5 8.1 8.6 2.3 2.5 4.2 8.6 12.4
Abbreviations: SD, standard deviation; IQR, interquartile range.
Table 2. Source Apportionment of Urban Background PM10 in Hong Kong, Indicated by the Percentage (%) a Source Explains the Variation of a Particular Chemical Constituent elemental carbon organic matter nitrate sulfate ammonium ion sodium ion potassium ion chloride ion aluminum arsenic calcium cadmium iron magnesium manganese nickel lead vanadium zinc
vehicle exhaust
soil and road dust
80.1 31.6 0.0 3.7 0.0 0.0 8.6 4.0 0.0 0.0 15.1 4.2 18.6 0.6 6.6 4.0 0.7 0.0 9.3
2.1 3.9 5.5 3.4 0.0 0.0 9.4 0.0 70.3 5.6 54.3 6.9 42.4 28.0 31.6 3.4 5.7 0.0 0.0
regional combustion residual oil 0.0 23.7 0.0 6.6 0.0 0.0 49.4 0.0 13.0 68.5 18.3 73.3 15.3 0.0 34.1 6.7 71.7 0.0 74.0
9.8 11.4 0.0 2.4 1.9 0.0 0.0 1.1 4.5 2.7 3.9 1.4 6.6 0.0 0.3 70.6 0.4 86.9 2.7
fresh sea salt
aged sea salt
secondary nitrate
secondary sulfate
2.6 2.1 1.6 0.0 1.4 19.9 4.5 86.2 0.5 2.5 2.5 2.8 0.9 15.1 0.7 1.9 1.5 1.3 0.0
5.4 0.0 12.2 25.7 0.0 80.1 2.9 0.0 1.0 0.0 5.5 2.6 0.7 54.7 4.8 8.2 3.3 11.8 11.7
0.0 19.2 80.8 0.0 24.3 0.0 9.0 8.7 2.3 3.2 0.4 0.0 8.6 0.0 8.1 3.1 6.0 0.0 2.3
0.0 8.2 0.0 58.1 72.4 0.0 16.3 0.0 8.4 17.4 0.0 8.9 6.9 1.7 13.9 2.1 10.8 0.0 0.0
was conducted to validate the source findings. All estimates are reported as percent (%) change in daily emergency hospital admissions for an interquartile range (IQR) increase in PM10 source. Where appropriate, 95% confidence intervals are presented. We performed all time-series analyses in the statistical environment R Software, version 3.0.2 (R Development Core Team, Vienna).
model, and calculating the cumulative effect from the sum of the single coefficient estimates.24 Total PM10 mass has been found to be both associated with respiratory events and differentially correlated with the constituent and source contributions.25 We ran sensitivity analysis to evaluate the net effect of each source on hospitalization, with PM10 mass simultaneously modeled at the same cumulative lag interval used for the predictor source. Multisource models were also conducted to model all sources simultaneously. Equivalent regression models were constructed and reanalyzed after using an alternative imputation method to impute missing sampling data. This method replaced the missing source contributions for days without PM10 samples with nonmissing measurement values from the previous day, instead of interpolation of missing data by linear regression. Exploratory analysis using daily mass data of PM2.5 and PM2.5−10 (calculated by subtracting PM2.5 from PM10) based on the same three temporal lag models as those for PM10 sources
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RESULTS
Air Pollution Levels and Respiratory Hospitalization. There were 659 963 emergency respiratory hospital admissions (226 admissions per day) in Hong Kong between 2001 and 2008 (Table 1). The daily mean temperature was 23.5 °C, and relative humidity was 78.1%; the average daily PM 10 concentration was 55.7 μg/m3 during the study period. An eight-factor solution provided the most feasible results. These factors represent daily PM10 mass contributions from eight sources of PM10, namely vehicle exhaust, soil/road dust, 3832
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Environmental Science & Technology Table 3. Pearson’s Correlation among the Estimated Sources of PM10
PM10 vehicle exhaust soil/road dust regional combustion residual oil fresh sea salt aged sea salt secondary nitrate secondary sulfate
PM10
vehicle exhaust
soil/road dust
regional combustion
residual oil
fresh sea salt
aged sea salt
secondary nitrate
1.00 0.48 0.59 0.84 0.37 0.09 0.10 0.76 0.78
1.00 0.23 0.50 0.30 −0.14 −0.23 0.32 0.30
1.00 0.42 −0.02 0.11 0.09 0.31 0.21
1.00 0.24 −0.07 −0.19 0.58 0.65
1.00 −0.11 −0.08 0.31 0.35
1.00 0.22 0.24 −0.16
1.00 0.06 −0.01
1.00 0.43
Figure 1. Percent change (95% CIs) in respiratory emergency hospital admissions per IQR increment in PM10 and its sources in Hong Kong, based on three temporal lag models (2001−2008). In single-lag models (solid triangles), the exposure variable was added to the model at the specified lag only; in the distributed-lag models (hollow squares), a third-degree polynomial constraint for lags from 0 to 5 was applied, whereas an unconstrained approach was used for cumulative lags 0−1, 2−5, and 0−5. All models were adjusted for meteorological factors, seasonal and temporal trend, day-ofweek, and influenza epidemic.
regional combustion, residual oil combustion, fresh sea salt, aged sea salt, secondary nitrate, and secondary sulfate.
Secondary sulfate took up the largest fraction (24.4%) of PM10 mass, followed by secondary nitrate (15.2%), vehicle 3833
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Table 4. Percent Change (95% CIs) in Emergency Hospital Admissions for Total Respiratory Diseases per IQR Increment in PM10 Sources Concentrations by Cumulative Lag Interval (from Unconstrained Distributed Lag Models) and Model Types pollutant vehicle exhaust
soil/road dust
regional combustion
residual oil
fresh sea salt
aged sea salt
secondary nitrate
secondary sulfate
distributed lag
single-pollutant modela
two-pollutant modelb
0−1 2−5 0−5 0−1 2−5 0−5 0−1 2−5 0−5 0−1 2−5 0−5 0−1 2−5 0−5 0−1 2−5 0−5 0−1 2−5 0−5 0−1 2−5 0−5
0.06 (−0.56, 0.69) 2.04 (1.23, 2.85)c 2.01 (1.04, 2.99)c 0.43 (0.04, 0.82)c 0.12 (−0.31, 0.56) 0.34 (−0.19, 0.87) 0.07 (−0.58, 0.72) 1.4 (0.63, 2.19)c 1.36 (0.43, 2.31)c 0.03 (−0.34, 0.4) 0.44 (−0.04, 0.92) 0.62 (0.04, 1.2)c 0.15 (−0.15, 0.46) −0.2 (−0.59, 0.2) −0.05 (−0.52, 0.42) 1.2 (0.63, 1.78)c −0.58 (−1.26, 0.11) 0.57 (−0.24, 1.4) 0.35 (−0.2, 0.9) 0.93 (0.25, 1.62)c 1.36 (0.55, 2.19)c 0.78 (0.23, 1.34)c 1.18 (0.52, 1.84)c 1.59 (0.82, 2.37)c
−0.47 (−1.17, 0.22) 1.68 (0.77, 2.59)c 1.19 (0.09, 2.31)c 0.2 (−0.23, 0.63) −0.33 (−0.82, 0.15) −0.32 (−0.9, 0.27) −1.81 (−2.82, −0.8) 0.56 (−0.71, 1.84) −0.88 (−2.36, 0.61) −0.4 (−0.83, 0.04) −0.04 (−0.59, 0.52) −0.12 (−0.78, 0.56) 0.09 (−0.22, 0.4) −0.27 (−0.67, 0.12) −0.17 (−0.64, 0.31) 1.15 (0.57, 1.72)c −0.77 (−1.45, −0.08) 0.28 (−0.55, 1.11) −0.48 (−1.24, 0.29) −0.06 (−1.02, 0.91) −0.03 (−1.18, 1.14) 0.26 (−0.58, 1.12) 0.55 (−0.42, 1.53) 0.54 (−0.59, 1.69)
a Single-pollutant models adjusting for meteorological factors, seasonal and temporal trend, day-of-week and influenza epidemic. bTwo-pollutant models with additional adjustment of PM10 mass. cp < 0.05.
distributed over time showed that the latency patterns of associations remained unchanged, though the magnitude and significance of many daily associations were reduced. Findings from unconstrained DLM demonstrated that PM10 from vehicle exhaust, regional combustion, and residual oil at cumulative lag2−5 were positively associated with 0.44%−2.04% increase in respiratory hospitalizations, whereas no evidence of association at lag0−1 was found. Statistically significantly positive associations were observed with aged sea salt (1.20%, 95% Cl: 0.63, 1.78) and soil/road dust (0.42%; 95% CI: 0.03, 0.82) at cumulative lag0−1, respectively. For PM10 originating from secondary nitrate and secondary sulfate, the elevation in hospitalization risk was prolonged. Overall, we found that increment in vehicle exhaust (per 4.6 μg/m3) was associated with the largest significant increase of 2.01% (95% Cl: 1.04, 2.99), followed by secondary sulfate (1.59%; 95% Cl: 0.82, 2.37), regional combustion (1.36%; 95% Cl: 0.43, 2.31), and secondary nitrate (1.36%; 95% Cl: 0.55, 2.19). In the sensitivity analysis, where we further adjusted for PM10 mass in two-pollutant models (Table 4), most of the positive and significant associations of respiratory hospitalizations with PM10 sources from single-pollutant models diminished. Exceptions include the associations with vehicle exhaust (1.68%; 95% CI: 0.77, 2.59) at lag2−5 and aged sea salt (1.15%; 95% CI: 0.57, 1.72) at lag0−1 days that remained significant in two-pollutant models. Findings from multipollutant models were similar to those from two-pollutant models (SI Figure S2). Nonetheless, the overall temporal pattern of associations remains fairly consistent across all models that combustion-related PM10 sources exhibited positive associations with respiratory hospitalizations at later cumulative lag days comparing to noncombustion sources.
exhaust (14.3%), and regional combustion (13.0%). Table 2 shows the detailed composition profiles of each estimated source. For instance, 80% of the variation in elemental carbon was explained by vehicle exhaust emission. Regional combustion emission was associated with large variation for a mixture of constituents, arising from wood/biomass burning (e.g., potassium ion) and coal combustion (e.g., arsenic, cadmium, lead, and zinc) in power plants and industrial facilities in the adjacent Pearl River Delta region, and these constituents cannot be further separated.26 Two sea salts were identified. Aged seas salt-related PM explained 80.1% of the variation in sodium ion, with negligible chloride ion loading, whereas fresh sea salt explained 86.2% of the chloride ion variation. This is consistent with previous studies that showed depletion of chloride ion from sea-spray particles through reaction with acidic substances, forming aged sea salt-related PM.27,28 Pearson correlation coefficients across PM10 and source-specific contributions are presented in Table 3; overall, correlations between sources were weak-to-moderate, with the highest correlation observed between regional combustion and secondary sulfate (r = 0.65). Associations with Respiratory Hospitalization. Figure 1 summarizes the regression results for each PM10 source based on three separate temporal lag analyses. When modeling each lag individually, PM 10 mass, vehicle exhaust, regional combustion, residual oil, and secondary particles exhibited similar pattern of associations in which larger risk estimates of respiratory hospitalization were observed with exposures on later days (lag3 or lag4). In contrast, PM10 from soil/road dust and aged sea salt were only positively linked to increase in hospitalizations at lag0 or lag1. Analysis using constrained DLM to investigate how the source-specific risk estimates were 3834
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Soil/road dust and aged sea salt were shown to be associated with elevated risk of respiratory hospitalizations; the association for aged sea salt persisted after adjusting for PM10 mass. Soil/ road dust particles are related to exposed soil, unpaved roads, and construction activities, and crustal materials in the background continental air mass.11 Aged sea salts are sea salt aerosols that have undergone chloride loss reactions in the atmosphere along coastal areas, possibly by reacting with nitric acid from the polluted urban air.28 Existing findings on the respiratory risks of soil and/or road dust are mixed;4,30 fewer studies that examined the associations between sea salt and respiratory health, including experimental study, reported significant links.36,37 We argue that the heterogeneity of findings may be explained partly by the application of PM2.5 composition for source apportionment in previous studies instead of PM10 composition that also comprises of larger crustal materials. Moreover, we found significant associations with respiratory hospitalizations for cumulative exposure to soil/road dust and aged sea salt at shorter lag days than their combustion counterparts. In two-pollutant models, respiratory hospitalization was significantly associated with vehicle exhaust at cumulative lag2−5, and with aged sea salt at cumulative lag0−1. This difference in lag structures may be due to the difference between combustion-related PM that are mostly particles of smaller diameter and PM representing soil/road dust and aged sea salt, which are abundantly found in the coarser fraction (i.e., PM2.5−10) generated by processes such as mechanical grinding and agricultural activities.38 Our speculation is supported by findings by Stafoggia et al.:23 using PM concentrations with different size fractions from 10 European cities, they showed clear associations of respiratory hospitalizations with PM2.5−10 at cumulative lag0−1 days, and with PM2.5 at cumulative lag2−5 days. Our exploratory analysis also strengthens this hypothesis by showing that PM2.5−10 exhibited a more immediate temporal association with respiratory hospitalization than PM2.5. Though precise patho-physiological mechanisms are not clear, working hypotheses include that PM, depending upon the emission sources, can induce inflammatory mediator release and oxidative stress in lung epithelial cells, decrements in lung function, and airway hyperreactivity, thereby causing respiratory symptoms and disease, including asthma exacerbation.39−41 The different temporal lag structures observed for PM10 sources may be due to different trigger mechanisms between particle size fractions. Particles of smaller diameter can penetrate deeper into the lung than coarser particles, causing inflammation in the lower respiratory tract and lung tissues, which could result in a more delayed observable response.42,43 In contrast, coarser particles deposit primarily on the upper respiratory tract,44 causing irritation and epithelial disruptions that might induce exacerbation of respiratory illnesses with shorter delay. Toxicological studies have associated coarse particles on a mass basis, rather than smaller particles, with higher hydroxyl radical generating capacity, greater cytokine production of macrophages and bacterial endotoxin content and thereby inflammatory responses;45−47 whereas smaller particles may be more potent if taking into consideration of particle number or surface area. It remains to be determined to what extent and the pathways in which coarse particle may induce more immediate health response than smaller particles. Further toxicological investigation on the temporal lag pattern and cumulative risk by PM size fraction are needed to allow for a more specific conclusion.
Exploratory analysis using daily Hong Kong PM mass data shows that PM2.5−10 exhibited a more immediate temporal association with respiratory hospitalization than PM2.5 (refer to SI Figure S3). Finally, risk estimates were not sensitive to alternative regression models in which another imputation method was applied to interpolate missing sampling data (data not shown).
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DISCUSSION To our knowledge, we conducted the first Asian study on associations between source-apportioned PM and respiratory events. We identified eight sources of PM10 mass in Hong Kong using source apportionment models, which were consistent with those previously reported using the same speciation data from 1998 to 2008.11 We found evidence of positive linkage between most PM10 sources and emergency respiratory hospitalizations for single-day lag periods up to 5 days prior to hospitalization. This is in contrast to a previous Copenhagen study that also examined the health impacts of PM10 sources among elderly but only found evidence for association of respiratory admissions with biomass and secondary inorganic.29 Our DLM findings also differ from prior work by Lall et al.,30 who assessed the cumulative effect of PM2.5 sources among Medicare enrollees in New York City and observed only association of PM2.5 steel with respiratory admissions over a consecutive 4-day exposure period. Heterogeneity in study findings might be due to the longer study period (8 years) and composite sample size in the present study, as well as the difference in pollution compositions, size fractions, exposure lags, and population susceptibility (e.g., different age distributions) between cities. Although single-pollutant models from our study yielded many significant associations, they should be cautiously interpreted as some effect estimates were no longer statistically significant after PM10 adjustment. However, we were still able to observe significant effects for vehicle exhaust and aged sea salt after accounting for PM10, and the overall temporal pattern of associations remains fairly consistent across all models. We observed positive associations between respiratory admissions and PM10 from vehicle exhaust, regional combustion, residual oil, and secondary particles; association with vehicle exhaust remains statistically significant in two-pollutant models adjusting for PM10. These combustion-related sources contributed to over 70% of ambient PM10 mass in Hong Kong. They primarily generate particles of smaller diameter (i.e., fine and ultrafine particles) through high-temperature combustion and atmospheric photochemical processes. Our findings are consistent with existing evidence of adverse effects of PM2.5 representing secondary sulfate, industrial sources, and traffic on respiratory hospital admissions and functions.4,31,32 Seagrave et al.31 compared acute cytotoxicity and inflammation effects of PM2.5 sources in rat lungs and found higher toxicity levels associated with motor vehicle and industrial sources; cellular stress response of human bronchial epithelial cells was found to highly correlate with residual oil combustion, but not with other noncombustion sources.32 Furthermore, previous epidemiologic studies have reported a latency of several days between PM2.5 exposure and respiratory events, possibly because of systemic inflammation and immune suppression.33−35 This is in accordance with our findings of significant associations with combustion-related sources at delayed exposure lags. 3835
DOI: 10.1021/es505030u Environ. Sci. Technol. 2015, 49, 3830−3838
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Environmental Science & Technology Our findings have clear implications for policies on air quality and emission control as well as public health. The road traffic density in Hong Kong was among the highest in the world at 254 vehicles per kilometer of road in 2009.48 Residual oil combustion is most likely related to marine vessel activities as Hong Kong port is one of the major international maritime centers in the world.49 Emissions of coal combustion and biomass burning, as well as gaseous precursor pollutants (e.g., nitrogen oxides) in the adjacent Pearl River Delta region have been the dominant contributors to particulate concentrations in Hong Kong through regional transportation. Our findings of increased health risks associated with these PM sources stress the importance of regulation and reduction of combustionrelated emissions, configuration of urban environment to reduce personal exposure, and establishment of a coordinated, regional-scale air quality management plan. Also, this study builds upon findings from previous studies, suggesting that either source- and constituent-based (e.g., elemental carbon for vehicle exhaust) air quality standard, in addition to standards for PM10 and PM2.5, might be an important next step toward better public health. For instance, measures aiming at controlling anthropogenic sources of coarse particles (e.g., soil/road dust and precursor gases) would be advisible. Several potential limitations should be taken into consideration. Exposure measurement error cannot be eliminated as a possibility. Chemical constituents with very low ambient concentrations (e.g., cadmium) might be subject to more instrument or laboratory errors. Likewise, constituents from local sources (e.g., elemental carbon from traffic) might be subject to more error than those regionally transported, given their higher spatial heterogeneity. Any resulting bias, however, is expected to be toward the null.50 Another limitation is that the application of PM10 speciation data did not allow us to determine clear differences in source contribution by size fraction. Quantitative measurement of composition or sources of coarse PM was not available, nor did we have a long enough period of PM2.5 composition data to subtract PM2.5−10 from PM10 composition measurements for source apportionment. Finally, we used the estimated source contributions directly in the health model, without accounting for the uncertainty in their estimation. Any underestimation of the resulting inferences, however, is expected to be small given the consistency in source identification across monitors and in effects obtained, and the agreement of our findings with previous studies.51 This study contributes to the available literature by providing evidence on the respiratory effects of source-apportioned particles. One strength is the use of source apportionment modeling, and our findings were consistent with those previously reported using the same speciation data from 1998 to 2008.11 The application of source-apportioned PM10 into the health models provided important insight in the respiratory associations with not only sources that primarily generate finer mode PM10, but also those that produce coarser mode PM10. Another strength is the assessment of the temporal lag structure of PM source-health associations, which might shed light to plausible biological mechanisms. Distributed lag analysis of source-apportioned PM was scarce in previous studies, because they often relied on composition data derived from networks using every third or sixth day sampling schedules. Also, most epidemiologic studies that examine the association between PM2.5 constituents/sources and health outcomes did not evaluate lags longer than 3 days, thus making it difficult to
assess the effect of exposure at longer day lags. Lastly, our study was well-powered to detect statistically significant associations, with two-thirds of a million emergency respiratory hospitalizations over 8 years. In summary, we found evidence that PM10 from vehicle exhaust, regional combustion, residual oil, and secondary particles at cumulative delayed exposure of 2−5 days were significantly associated with elevated respiratory hospitalization risks in Hong Kong. Consecutive exposure of 0−1 days to soil/ road dust and aged sea salt were also linked to respiratory hospitalizations. Findings from our study would help prioritize research on the biologic mechanisms linking particulate pollution to pulmonary health and guide future monitoring and emission control polices.
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ASSOCIATED CONTENT
* Supporting Information S
This material is available free of charge via the Internet at http://pubs.acs.org/.
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AUTHOR INFORMATION
Corresponding Author
*Phone: (+852) 2831 5071; fax: (+852) 2855 9528; e-mail:
[email protected] (L.T.). Present Addresses §
Department of Health Sciences, Northeastern University, Boston, Massachusetts, U.S.A. ∥ School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region of the People’s Republic of China. Author Contributions
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. V.C.P., L.W.T., I.T.S.Y. defined the research theme; V.C.P. conducted the analysis, interpreted the results, and wrote the manuscript. M.A.K. and H.Q. interpreted the results and assisted on paper writing. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors thank the Hong Kong Environmental Protection Department for providing air pollution data, the Hong Kong Observatory for providing meteorological data, and the Hospital Authority for providing hospital admission data.
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ABBREVIATIONS PM10 particulate matter with aerodynamic diameter of 10 μm or less PM2.5−10 particulate matter with aerodynamic diameter of 2.5 μm or less
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REFERENCES
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