Relationship between particulate matter measured by optical particle counter and mortality in Seoul, Korea, during 2001.
Publication: Journal of Environmental Health
Date: Monday, September 1 2008
Introduction
Particulate matter (PM) is a complex mixture of particles suspended in the air that vary in size and composition. On the basis of recent findings, many government agencies have re-evaluated regulatory standards or guidelines for levels of PM in the air (Lipp-mann, Ito, Nadas,
Methods
Study Area
We selected Seoul, Korea, as a study area. Seoul, centrally located in the Korean peninsula, is the biggest metropolitan area in the country (Figure 1). During the study period, the mean population size was about 9.8 million, the mean size of the elderly population was approximately 0.7 million, and traffic density per year was around 2.9 billion vehicles. The major air pollution sources were automobile exhaust emissions and domestic heating. Seoul has a four-season climate and an annual temperature range of -11.1[degrees]C to 30.0[degrees]C.
[FIGURE 1 OMITTED]
Mortality and Weather Data
The number of deaths occurring in Seoul between January 1 and December 31, 2001, according to the day on which the deaths occurred, was supplied by the National Statistics Office of Korea. Since 1995, the National Statistics Office has followed the International Classification of Disease, 10th Revision (ICD-10). Deaths due to accidents were excluded, as were all deaths of residents outside Seoul. The daily number of deaths from all respiratory diseases and all cardiovascular disease were calculated. Information on the 24-hour average temperature ([degrees]C) and relative humidity (%) of the same calendar year was available from the Korea Meteorological Administration.
Particulate Matter Data Using Monitoring Stations
[PM.sub.10] (particulate matter less than 10 [micro]m in diameter) data were provided by the Ministry of Environment of the Republic of Korea. Exposure measurements during the study period were taken from 27 monitoring sites, which provide hourly measurements of [PM.sub.10] ([beta]-ray absorption method). We calculated the hourly mean [PM.sub.10] level from the 27 monitoring stations and then computed their 24-hour averages. [PM.sub.2.5] (less than 2.5 [[micro]m] in diameter) data were provided by the Seoul Metropolitan Research Institute of Public Health and Environment of Korea. Exposure measurements during the study period were taken from one monitoring site, which provided daily measurements (gravimetric method) using a Mini volume air sampler (AirMetrics [TM], Eugene, OR).
Particulate Matter Data Using OPC
The number concentrations of particulate matter were measured using an OPC (HIAC/ROYCO 5230). This instrument was set on the roof of a building. Air was sampled from the air intake at a height of 12 meters to a cylindrical chamber outside the building and then led to the instrument. Inside the chamber, flow rate was lowered and very large particles were excluded to protect the instrument. The light source was a semiconductor laser with the optical system set at 90[degrees] scattered. The OPCs were operated in the dynamic range of 0.3-25 [micro]m with seven cutoff diameters: 0.5, 0.82, 1.35, 2.23, 3.67, 6.06, and 10 [micro]m. The eight ranges were divided equally on the log-decimal scale, except the last one. We recalculated the daily number concentration of particulate matter using three-minute-averaged data for every hour from January 1 to December 31, 2001.
Data Analysis
A generalized additive model (GAM) that used nonparametric smoothing was applied to allow for highly flexible fitting of seasonality and long-term time trends, as well as nonlinear associations with weather variables such as air temperature and relative humidity (Pope and Kalkstein, 1996; Pope and Schwartz, 1996). Therefore, we applied generalized additive Poisson regression models (GAMs), which include nonparametric smooth functions to control the potential nonlinear dependence of daily time-trends and weather variables on the logarithm of the mortality. We used the following basic model:
log[E(Y)] = [[beta].sub.0]X + [S.sub.i]([Z.sub.i]) + .... + [S.sub.p]([Z.sub.p])
Where Y is the daily count of deaths, X is the particulate matters level, [Z.sub.i] represents the time and meteorological variables, and [S.sub.i] represents the loess smooth functions. [Z.sub.i] values cover temperature, relative humidity on the day on which deaths occurred, the previous day's temperature, time trends, and the day of the week. The regression coefficients were estimated using GAMs, and the variances were estimated robustly. Regression equations were calculated in GAM CONTROL of S-PLUS software.
Long-term temporal variations were controlled using the generalized additive model. We introduced weather variables into the model to allow the mortality predictions to be adjusted for both air temperature and relative humidity. Also, daily mortality figures were fitted to the generalized additive model, which included a locally weighted running-line smoothing (loess) function for time, to capture seasonal and long-term trends. The modeling strategy was a systematic approach, building from simple to more complicated models with an increasing number of covariates (Table 1). We first incorporated nonlinear time and weather terms into the generalized additive models. After controlling for time and weather, the particulate matter variable was introduced to the model. In addition, we considered the lag effects of temperature, humidity, and PM concentrations in building the models. To take the lag effect into consideration, we utilized a distributed-lag model for each cause of death to verify and compare the lag-effect window pattern. Distributed-lag models have been used recently as an analytical approach in the study of epidemiology associated with air pollution (Schwartz, 2000). The unconstrained distributed-lag model, which assumes that the number of deaths on any one day depends on the individual PM concentrations of the same day, one to seven lagged days, or moving averages from two to three days. The generalized additive models were used with a more stringent convergence criterion (than the default values of S-plus) to avoid biased estimates of regression coefficients and standard errors (Dominici, McDermott, Zeger, & Samet, 2002; Ramsay, Burnett, & Krewski, 2003). To compare the relative quality of the mortality predictions across these non-nested models, Akaike's Information Criterion (AIC) was used as a measure of how well the model fitted the data (Akaike, 1970; Hastie & Tibshirani, 1990). Smaller AIC values indicate the preferred model. All analyses were carried out using both SAS (SAS Institute, Cary, NC) and S-plus (Statistical Sciences, Seattle, WA).
TABLE 1 AIC (a) for Model Building for Total Death Count Total Death Count (All Ages) Total Death Count (The Elderly) Description of Model AIC Description of Model AIC Time Time [T.sub.1]: smoothed 381.4049 [T.sub.1]: smoothed 398.8702 function of date function of date [T.sub.2]:[T.sub.1] 373.5875 + indicator variables for day of week Weather Weather [W.sub.1]:[T.sub.2] 368.0204 [W.sub.1]:[T.sub.1] 376.8473 + smoothing function + smoothing function of TEMP3 (b) of TEMP3 [W.sub.2]:[W.sub.1] 359.9553 [W.sub.2]:[W.sub.1] 373.4097 + smoothing function + smoothing function of HUMD1 (c) of HUMD1 Pollutant Pollutant [P.sub.1]:[W.sub.2] 335.9970 [P.sub.1]:[W.sub.2] 338.6557 + [CH.sub.234567] + [CH.sub.234567] (a) AIC: Akaike's information criterion. (b) TEMP3: Air temperature three days before. (c) HUMD1: Relative humidity one day before.
Results
In our results, fine particle and respiratory particle number concentration using OPC show a weak correlation with [PM.sub.2.5] and [PM.sub.10] mass concentration data from monitoring stations (correlation coefficients 0.45 and 0.41, respectively; data not shown). Our results show [PM.sub.2.5] mass concentrations constituted 42.99% of [PM.sub.10] mass concentrations, but fine particle number concentrations constituted 99.70% of respiratory particle number concentrations.
Table 2 shows summary statistics of the daily death counts by specific causes, particulate matters (number concentration and mass concentration), and weather information in Seoul from January 1 to December 31, 2001. On average, 102.48, 5.50, and 25.03 persons died of all non-accidental causes, respiratory causes, and cardiovascular causes, respectively, each day in the city over the study period. In the elderly (aged over 65 years), an average of 60.53, 4.44, and 17.59 persons died of all causes, respiratory causes, and cardiovascular causes, respectively. The 24-hour average number concentration of [PM.sub.2.5] ([CH.sub.234]), number concentration of [PM.sub.10] ([CH.sub.234568]), mass concentration of [PM.sub.2.5] and mass concentration of [PM.sub.10] were 12.58 number/[cm.sup.3] (the total number of particles per cubic centimeter), 12.72 number/[cm.sup.3], 34.79 [micro]g/[m.sup.3], and 72.47 [micro]g/[m.sup.3], respectively.
TABLE 2 Summary Statistics for Daily Deaths, Particulate Matters, and Weather in Seoul, Korea, 2001 Variables N (a) Mean [+ or -] SD (b) Min (c) 10% Death counts (persons) All causes All aged 365 102.48 [+ or -] 11.24 70 87 The elderly 365 60.53 [+ or -] 8.79 36 50 Respiratory causes All aged 365 5.50 [+ or -] 2.49 0 3 The elderly 365 4.44 [+ or -] 2.21 0 2 Cardlovascular causes All aged 365 25.03 [+ or -] 5.68 11 18 The elderly 365 17.59 [+ or -] 4.57 6 12 OPC (e) data (number/ [cm.sup.3]) C[H.sub.234] 337 12.58 [+ or -] 7.73 1.32 4.16 (0.50 - 2.23 [mu]m) C[H.sub.567] 337 0.14 [+ or -] 0.17 0.01 0.04 (2.23 - 10.00 [micro] m) C[H.sub.234567] 337 12.72 [+ or -] 7.81 1.34 4.21 (0.50 - 10.00 [micro] m) Mass data ([mu]g/ [m.sup.3] P[M.sub.2.5 278 34.79 [+ or -] 25.66 2.29 8.83 (less than 2.5 [micro] m) P[M.sub.10 278 72.47 [+ or -] 44.79 13.99 30.84 (less than 10 [micro] m) Weather ([Degrees]C, %) Temperature 365 12.86 [+ or -] 10.99 -15.50 -2.60 ([Degrees]C) Relative 365 60.91 [+ or -] 13.97 26.10 42.00 humidity (%) Variables 25% 50% 75% 95% Max (d) Death counts (persons) All causes All aged 95 102 110 117 146 The elderly 55 60 66 72 85 Respiratory causes All aged 4 5 7 9 15 The elderly 3 4 6 7 12 Cardlovascular causes All aged 21 24 29 33 42 The elderly 14 17 20 24 32 OPC(e) data (number/c[m.sup.3]) C[H.sub.234] (0.50 - 6.66 10.60 16.87 24.24 40.25 2.23 [micro] m) C[H.sub.567] (2.23 - 0.06 0.10 0.16 0.24 1.84 10.00 [micro] m) C[H.sub.234567] 6.73 10.72 17.10 24.45 41.22 (0.50 - 10.00 [micro] m) Mass data ([mu]g/[m.sup.3] P[M.sub.2.5 (less 15.90 29.40 45.25 67.29 159.38 than 2.5 [micro] m) P[M.sub.10 (less 43.96 62.85 84.40 127.12 374.20 than 10 [micro] m) Weather ([Degrees]C, %) Temperature 2.90 15.50 22.60 26.00 30.00 ([Degrees]C) Relative 51.30 60.10 71.30 79.60 92.10 humidity (%) (a) N: Number of days. (b) SD: Standard deviation. (c) Min: Minimum. (d) Max: Maximum. (e) OPC: Optical particle counter.
Our graphic analysis indicated that there was a relatively linear positive relationship between the daily cardiovascular mortality count and the log-transformed mass concentrations of [PM.sub.2.5] up to approximately 100 [micro]g/[m.sup.3].
Table 3 shows estimated percentage increases in the relative risk and 95% confidence intervals associated with interquartile range increases in the daily average number concentrations and mass concentrations after controlling for temporal trends, meteorological variables, and days of the week in the single-pollutants model. Distributed lag models always have smaller AIC values and larger estimated relative risks than time-series models. The IQR increase of fine particle (10.21 numbers/[cm.sup.3]) and respiratory particle (10.38 number/[cm.sup.3]) number concentration were associated with a 5.73% (5.03%-6.45%) and a 5.82% (5.13%-6.53%) increase in respiratory disease-associated mortality, respectively. The estimated effects on mortality from respiratory causes were considerably greater than those for other causes of mortality. The equivalent effects in the elderly were more than 0.51% to 2.59%, and the relative risks of respiratory-related and cardiovascular-related mortality were more than 0.51% to 1.06% compared with all-cause mortality. In addition, the results for mass concentration of [PM.sub.2.5] and [PM.sub.10] were similar to those for number concentration.
TABLE 3 Relative Risk of Cause-Specific Mortality for Particulate
Matters
Particle Matters All-Causes Deaths Respiratory-Cause Deaths
All Ages The All Ages The
Elderly Elderly
OPC data (size
fraction)
[CH.sub.234](0.50 - 0.81 1.05 2.54 5.73
2.23 [micro] m) (0.68 to (0.88 to (1.94 to (5.03 to
0.95) 1.22) 3.14) 6.45)
[CH.sub.567] 0.56 0.84 1.54 1.85
(2.23 -10.00 [micro]m) (-0.05 to (-0.06 to (-0.19 to (-0.21 to
6.11) 8.16) 28.07) 31.95)
[CH.sub.245678] 0.82 1.06 2.59 5.82
(0.50 -10.00 [micro] m) (0.69 to (0.89 to (2.00 to (5.13 to
0.95) 1.22) 3.19) 6.53)
Mass data (size
fraction)
[PM.sub.2.5](less 0.89 1.51 6.59 8.14
than 2.5 [micro] m) (0.84 to (1.44 to (6.38 to (7.90 to
0.94) 1.58) 6.80) 8.38)
[PM.sub.10] (less 1.07 1.07 2.56 5.14
than 10 [micro] m) (1.04 to (1.04 to (2.46 to (5.03 to
1.09) 1.10) 2.65) 5.25)
Particle Matters Cardiovascular-Cause Deaths
All Ages The Elderly
OPC data
(size fraction)
[CH.sub.234] 1.97 (1.68 to 2.25) 5.39 (5.06 to 5.72)
(0.50 - 2.23 [micro]m)
[CH.sub. 567] 1.22 (-0.10 to 13.84) 2.26 (-0.10 to 15.97)
(2.23 - 10.00 [micro] m)
[C H.sub.245678] 2.03 (1.75 to 2.31) 5.46 (5.13 to 5.78)
(0.50 - 10.00 [micro] m)
Mass data
(size fraction)
[PM.sub.2.5] (less 1.94 (1.83 to 2.05) 4.24 (4.11 to 4.73)
than 2.5 [micro] m)
[PM.sub.10] (less 1.87 (1.82 to 1.91) 3.83 (3.78 to 3.89)
than 10 [micro] m)
Note. This table shows that estimated percentage increases in the risk
of death and 95% confidence intervals associated with IQR
(interquartile range) increases in the daily average particulate
matters using OPC (optical particle counter), [PM.sub.2.5] measured at
individual monitoring site, and [PM.sub.10] from national monitoring
stations with selected best lag time using single-pollutant model
(these models include the following variables for day of week and
smooth spline function of date, temperature, and relative humidity as
well IQR of [CH.sub.234], [CH.sub.567], [CH.sub.2345678], [PM.sub.2.5],
and [PM.sub.10] are 10.21 number/[cm.sup.3], 0.10 number/[cm.sup.3],
10.37 number/[cm.sup.3], 29.35[micro]g/[m.sup.3], and 40.44
[micro]g/[m.sup.3], respectively) in Seoul, Korea.
Discussion
In our study, the number concentration and mass concentration of particulate matter were significantly associated with an increased risk of mortality related to respiratory and cardiovascular disease among the elderly. Moreover, our findings indicated that the elderly are generally more susceptible to the effects of number concentration as well as mass concentration in particulate matter.
Understanding how the number concentration of particles containing metals changes as a function of particle size, time, and location may help to characterize the sources of these emissions and the health effects they induce (Yakovleva, Hopke, & Wallace, 1999). Toxicological studies also have concluded that ultrafine particles (particles less than 100 nm in diameter) are comparatively more toxic than larger particles with identical chemical composition and mass (MacNee & Donaldson, 2003; Oberdorster, 1996). Hypotheses have been proposed which link adverse health effects with the number concentration of particulate matter (Laden, Neas, Dockery, & Schwartz, 2000; Pekkanen, Timonen, Ru-uskanen, Reponen, & Mirme, 1997; Peters, Wichmann, Tuch, Heinrich, & Heyder, 1997) rather than the total mass measurement. However, the number of studies on the relationship between mortality/morbidity and number concentration of particulate matter is limited. It is still unknown whether the observed PM-related health effects are related to particle number, particle surface area, particle mass, or particle chemical composition (Sardar, Fine, Yoon, & Sioutas, 2004). Studies on rodents show that the inflammatory response is more prominent when ultrafine particles are administered compared with larger particles (Oberdorster, 2001), suggesting either a particle number or surface area effect. Toxicological studies have also shown that ultrafine particles have higher oxidative stress potential and can penetrate and destroy mitochondria within epithelial cells (Li et al., 2003). Penttinen and co-authors tested the hypothesis that high numbers of ultrafine particles in the atmosphere can induce alveolar inflammation and exacerbation of pre-existing cardiopulmonary diseases (Penttinen et al., 2001). They found that daily mean number concentration and peak expiratory flow (PEF) are negatively associated and that the effect is the most prominent with particles in the ultrafine range. Another study found associations between the number concentrations of ultrafine PM and lowered PEF among asthmatic adults (Peters, Wichmann, Tuch, Heinrich, & Heyder, 1997).
Recently, Dominici and colleagues reported that a parameter estimate could be biased upward when using the GAM method with default convergence parameters, and they suggested that the use of GAMs requires extreme caution and the imposition of stricter than usual convergence criteria (Dominici, McDermott, Zeger, & Samet, 2002). In our current time-series study of air pollution effects, we used the GAM method with moving regression smoothing (loess), and default parameters, to conduct nonlinear regression analysis.
The Poisson regression model included time trends, day of the week, and weather variables. Generally, the estimated effects of the lags were large when AIC values were small and the effect patterns resulting from respiratory and cardiovascular mortality showed little difference. To determine the lag effects of particle number on mortality, the relative risks of different lag models, with a maximum lag of seven days, were compared. For respiratory mortality, the estimated effect of lag 2 is the largest and the AIC value is the smallest, so we concluded that respiratory mortality rates are more affected by air pollution levels on the previous two days. However, we concluded that cardiovascular mortality was more affected by air pollution levels during the previous six days. Lag structures in the relationship between air pollution and respiratory and cardiovascular mortality have been reported in several studies. Some studies report a shorter lag period for respiratory mortality and some report the reverse. In the study of Goldberg and co-authors in Montreal, respiratory mortality (specifically of persons more than 65 years old) was higher following same-day exposure (Goldberg et al., 2001). Cardiovascular disease was more affected by exposure on the previous day. Our study shows similar results. Braga and co-authors, however, reported that cardiovascular mortality represented an acute response to exposure on the one or two previous days (Braga, Zanobetti, & Schwartz, 2001). Kim and colleagues reported that these different results can be explained by the following. First, the mixture of particles is likely to vary with study area in size distribution, number, and chemical composition. The toxicity of particulate matter depends on its chemical composition and size distribution (Kim, Kim, & Hong, 2003). Fine particles([PM.sub.2.5]) have been found to have greater effects on health than [PM.sub.10] (Bremner et al., 1999; Lee, Shin, & Chung, 1999). The [PM.sub.2.5] fraction in [PM.sub.10] is likely to be dissimilar in different geographical regions (Chow et al., 1996). Our results shows [PM.sub.2.5] mass concentrations constituted 42.99% of [PM.sub.10] mass concentrations, and Chow and co-authors reported [PM.sub.2.5] mass concentrations constituted 30% to 70% of [PM.sub.10] mass concentrations at 10 sites in central California (Chow et al., 1996). Second, the different populations have different structures. Some studies reported different susceptibilities for different age groups (especially infants and the elderly) (Ha et al., 2001). It is quite reasonable to assume that the actual effects are different for different study populations.
In addition to the other limitations of this study, first, we did not consider individual levels in the model. Measurement errors resulting from differences between the population-average exposure and ambient levels cannot be avoided. This kind of measurement error, however, is known to cause a bias toward the null and underestimation of the effects of pollution. Moreover, only one monitoring site provided data for the [PM.sub.2.5] particle and number particle. Our results reflected a practical reason rather than a representative reason of Seoul's [PM.sub.2.5] concentrations and number concentration level. Second, we identified only the elderly as a susceptible group because we have no data to make the identification of other susceptible groups, such as lower-income groups or preexisting respiratory or cardiovascular disease groups. We also have not identified children as a susceptible group because of their various activity patterns. Third, correlations between particle number and mass concentration are poor in our results. Fine particles, although having low mass in ambient air, exist at high number concentrations (Woo, Chen, Pui, & McMurry, 2001). Correlations between particle number and mass concentration are poor (Woo, Chen, Pui, & McMurry, 2001), suggesting that the number concentrations of these particles may be an important matrix that needs to be considered separately in epidemiological investigations.
In our study, particulate matter number concentration and mass concentration were associated significantly with an increased risk of respiratory and cardiovascular disease-related mortality among the elderly. Our findings indicate that the elderly are generally more susceptible to the effects of number concentration as well as mass concentration of particulate matter, which is consistent with the conclusions drawn from our previous mortality study (Lee et al., 2003). These findings support the hypothesis that air pollution is harmful to sensitive subjects, such as the elderly, and has a greater effect on respiratory-and cardiovascular-related mortality than all-cause non-accidental mortality. Also, our results using OPC data do not support the hypothesis that the [PM.sub.2.5] has more adverse health effects than [PM.sub.10] in number concentration but not in mass concentration.
In conclusion, the particle number in ambient air was significantly associated with respiratory-related and cardiovascular disease-associated mortality and is more harmful to the elderly in Seoul. These associations were observed after taking into account time trends, day of the week, and meteorological variables. These findings support the possibility that acute pathogenic processes in the respiratory and cardiovascular system could be induced by the particle number in ambient air.
Acknowledgements: This research was supported by the Ministry of Environments, Re public of Korea (Eco-technopia 2004).
Corresponding Author: Jong-Tae Lee, Department of Public Health, Graduate School of Hanyang University, Hanyang University, 17 Haengdang-Dong, Seongdong-Gu, Seoul 133-791, Korea. E-mail: jlee@hanyang.ac.kr.
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Yong-Sung Cho, M.Sc., Ph.D.
Jong-Tae Lee, M.Sc., Ph.D.
Chang-Hoon Jung, M.Sc., Ph.D.
Young-Sin Chun, M.Sc., Ph.D.
Yoon-Shin Kim, M.P.H., Dr.H.Sc., Ph.D.
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