Short-term effects of daily air pollution on mortality

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Wan Rozita Wan Mahiyuddin

^a

, Mazrura Sahani

^b^,^*

, Rasimah Aripin

^c

, Mohd Talib Latif

^d

, Thuan-Quoc Thach

^e

, Chit-Ming Wong

^e

aInstitute for Medical Research, Jalan Pahang, 50588 Kuala Lumpur, Malaysia

bFaculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, 50300 Kuala Lumpur, Malaysia

cCentre of Statistical Studies, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

dSchool of Environmental and Natural Resource Sciences, Faculty of Science Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia

eDepartment of Community Medicine, School of Public Health, The University of Hong Kong, 5/F William MW, Mong Block, 21 Sassoon Road, Hong Kong, China

h i g h l i g h t s

<We model the association of air pollution and mortality using Poisson regression.

<RR for each pollutant was obtained for every IQR increase at different lag times.

<O₃, CO and PM₁₀were the important pollutants associated with natural mortality.

<All pollutants except SO2were associated with respiratory mortality.

<Those who have respiratory diseases are at higher risk of mortality.

a r t i c l e i n f o

Article history:

Received 20 March 2012 Received in revised form 12 October 2012 Accepted 12 October 2012

Keywords:

Relative risk Health Mortality O3

CO PM10

Klang Valley Malaysia

a b s t r a c t

The daily variations of air pollutants in the Klang Valley, Malaysia, which includes Kuala Lumpur were investigated for its association with mortality counts using time series analysis. This study located in the tropic with much less seasonal variation than typically seen in more temperate climates. Data on daily mortality for the Klang Valley (2000e2006), daily mean concentrations of air pollutants of PM10, SO2, CO, NO2, O3, daily maximum O3and meteorological conditions were obtained from Malaysian Department of Environment. We examined the association between pollutants and daily mortality using Poisson regression while controlling for time trends and meteorological factors. Effects of the pollutants (Relative Risk, RR) on current-day (lag 0) mortality to seven previous days (lag 7) and the effects of the pollutants from thefirst two days (lag 01) to thefirst eight days (lag 07) were determined. We found significant associations in the single-pollutant model for PM10 and the daily mean O3 with natural mortality. For the daily mean O3, the highest association was at lag 05 (RR¼1.0215, 95%

CI¼1.0013e1.0202). CO was found not significantly associated with natural mortality, however the RR’s of CO were found to be consistently higher than PM10. In spite of significant results of PM10, the magnitude of RR’s of PM10was not important for natural mortality in comparison with either daily mean O3or CO. There is an association between daily mean O3and natural mortality in a two-pollutants model after adjusting for PM10. Most pollutants except SO2,were significantly associated with respiratory mortality in a single pollutant model. Daily mean O3is also important for respiratory mortality, with over 10% of mortality associated with every IQR increased. Thesefindings are noteworthy because seasonal confounding is unlikely in this relatively stable climate, by contrast with more temperate regions.

1. Introduction

Many researchers have studied the impact of air pollution on human health and have demonstrated links between air pollution

and mortality (Dockery et al., 1993; Schwartz, 1994; Pope et al., 2002). Such studies have inﬂuenced the implementation of air quality controls to protect public health (Holland and Pye, 2006;

USEPA, 2004;WHO, 2006). Daily variations in concentrations of air pollutants have been shown to have a relationship with variations in mortality counts (Brunekreef and Holgate, 2002) in different parts of the world (Samet, 2002;Hoek et al., 2002;Fischer et al., 2011).

*Corresponding author. Tel.:þ60 3 26878136; fax:þ60 3 26878137.

E-mail address:[email protected](M. Sahani).

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Time-series studies are epidemiological approaches used to determine statistical associations between ambient concentrations of pollutants and other environmental parameters on human health. An extension of time-series studies, the aggregate risk index (ARI), has been established to assess additive effects of short-term exposure to the major air pollutants (Sicard et al., 2011, 2012).

Individual factors are unlikely to cause bias in time-series studies, although they may have an inﬂuence on panel and other longitu- dinal studies (Navidi et al., 1999;Zeger et al., 1999, 2000;Schwartz and Zanobetti, 2000;Levy et al., 2001;Bateson and Schwartz, 2001;

Bell et al., 2004;Burnett and Goldberg, 2003;Dominici et al., 2000, 2003, 2004; Goldberg et al., 2004; Armstrong, 2006). Previous coordinated air pollution time-series projects include Air Pollution and Health: A European Approach (APHEA) (Katsouyanni et al., 1996), the National Morbidity, Mortality and Air Pollution Study (NMMAPS) (Samet et al., 2000) and Air Pollution and Health: A Combined European and North American Approach (APHENA) (Samoli et al., 2008). Most time-series studies have shown that daily mortality is positively associated with short-term effects of atmospheric pollutants. This positive association was reported for ongoing and previous day (lag 1) toﬁve days’(lag 5) exposure to air pollution (Morris et al., 1995; Anderson et al., 1996; Michelozzi et al., 1998; Schwartz and Zanobetti, 2000). In some studies undertaken in Asia, however, the highest single day associations with natural mortality occurred at either lag 0 or lag 1 day, whereas respiratory mortality occurred at lag 2 days (Wong et al., 2001, 2008a). Other studies have shown that the highest signiﬁcant association between pollutants and mortality was at the mean of the current day and the previous day (lag 01) (Nuntavarn et al., 2008;Kan et al., 2008;Qian et al., 2008).

Various studies focussing on mortality in relation to air pollution have been conducted. In 2010, the Committee on the Medical Effects of Air Pollutants publishedﬁndings on mortality effects of particulate air pollution in the United Kingdom (COMEAP, 2010).

Similar studies have been published for Inchon in Asia (Hong et al., 1999); Seoul and Ulsan, South Korea (Lee et al., 1999; Lee and Schwartz, 1999; Kwon et al., 2001); Shenyang, China (Xu et al., 2000); seven cities in South Korea (Lee et al., 2000); New Delhi, India (Cropper et al., 1997) and Bangkok (Nuntavarn et al., 2008) as part of the framework of theﬁrst Public Health and Air Pollution in Asia (PAPA) project (Wong et al., 2008b). This PAPA project used a common protocol adopted and modiﬁed from previous coordinated time-series studies, such as APHEA (Katsouyanni et al., 1996), APHENA (Katsouyanni et al., 2009) and NMMAPS (Dominici et al., 2003). The PAPA project found that effects of particulate pollutants in Asian cities were similar to or greater than those observed in most North American and Western European cities, despite large differences in concentrations and that effects of gaseous pollutants in Asian cities were noticeably higher (Wong et al., 2008b). Based on a systematic search of the Asian literature (Public Health and Air PollutioneScience Access on the Net), the Health Effect Institute (HEI, 2010) reported that most studies on health effects of air pollution were undertaken in China and Japan, with fewer in South Asia. Studies in Bangladesh, Cambodia, Laos, Singapore and Malaysia remain underrepresented.

In Malaysia, these studies were limited due to a lack of inte- gration with local environmental and epidemiological data. Only four studies (Sahani et al., 2001;Sastry, 2002;Keywood et al., 2003;

Mott et al., 2005) have been published in Malaysia, with all the aforementioned studies related to the 1997 transboundary haze episode. In this study, we conducted a time-series study of the relationship between air pollution and mortality in the Klang Valley, a heavily industrialised urban area in Malaysia using the common PAPA protocol (HEI, 2006; R Development Core Team, 2008). This study aims to estimate short-term health effects of

pollutants on mortality in the Klang Valley over a 7-year period, 2000e2006.

2. Methodology

2.1. Study area

The Klang Valley is a basin located on the west coast of the Malaysian Peninsular, which is surrounded by highlands exceeding an altitude of 1500 m to the east and by the Straits of Malacca to the west (Abas and Simoneit, 1996;Omar et al., 2002). This is a tropical location, with much less seasonal variation than typically seen in more temperate climates. The area of the Klang Valley is approxi- mately 2,832 km², comprising Kuala Lumpur and its suburbs, as well as the adjoining cities and towns of the state of Selangor (Fig. 1).

The Klang Valley is considered to be the heartland of Malaysia’s industry and commerce (Azmi et al., 2010). Due to its location and widespread development, which resulted in rapid urbanisation, population growth and industrial activities, the Klang Valley is constantly exposed to pollutants, which cause air quality problems (Rashid and Rahmalan, 1993; Rashid, 1993;Rashid and Grifﬁths, 1995). Motor vehicles are the main source of major air pollutant emissions in the Klang Valley (Afroz et al., 2003). Its geographical location and weak prevailing winds contribute to stable atmospheric conditions, which, in turn, lead to air pollutants accumu- lating and stagnating in the air (DOE, 2006). In 2006, the estimated population exceeded 4.7 million, with over 1.5 million people inhabiting Kuala Lumpur (DOS, 2006).

2.2. Mortality data

Daily mortality records from 2000 to 2006 were obtained from the Department of Statistics, Malaysia. We focused on mortality from all natural causes across all age ranges, with natural mortality deﬁned as all-cause mortality excluding external causes. The coding adopted for mortality adhered to the International Classi- ﬁcation of Disease, Tenth Revision (ICD-10) by the World Health Organization (WHO) for health outcomes: all natural mortality, A00eR99; cardiovascular mortality, I00eI99; and respiratory mortality, J00eJ98.

2.3. Air quality and meteorological data

The air quality data (January 2000eDecember 2006) used for this study were obtained from the Air Quality Division of the Department of the Environment, Malaysia (DOE) through long- term monitoring undertaken by a private company, Alam Sekitar Sdn Bhd (ASMA) which met quality assurance and quality control procedures (Azmi et al., 2010). The air pollutant parameters utilised in these studies were particulate matter with an aerodynamic diameter size below 10m^{m (PM}10), carbon monoxide (CO), sulphur dioxide (SO₂), nitrogen dioxide (NO₂) and ozone (O₃). The daily mean of the concentrations of the pollutants was used to represent the daily reading for the whole of the Klang Valley. In the majority of epidemiological studies on health effects of air pollution, the daily maximum concentration of O₃has been used (Morris et al., 1995; Wong et al., 2008a), as well as the daily mean of O3

(Michelozzi et al., 1998;Wong et al., 2008b). For this study, the 8-h mean concentrations and the daily maximum concentrations were analysed. Daily meteorological parameters such as temperature, relative humidity and rainfall were obtained from the Meteoro- logical Service Department.

Of 51 continuous air quality monitoring (CAQM) stations throughout Malaysia, 37 are located on Peninsular Malaysia. Six of

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these stations were included in this study, namely, Kuala Lumpur, Petaling Jaya, Shah Alam, Gombak, Kajang and Klang (Table 1, Fig. 1).

2.4. Air pollution data and missing values

The percentage of validated data for each pollutant was: CO (91.0%), O₃(90.0%), NO₂(90.4%), SO₂(94.0%) and PM₁₀(91.3%). The presence of missing data in any of the stations was imputed using the APHEA’s method before the overall mean of the Klang Valley

data was calculated (Katsouyanni et al., 1996). A similar method has also been adopted in previous studies (Wong et al., 2008a, 2008b).

2.5. Statistical analysis

The analytical method was adapted from the common PAPA protocol (Wong et al., 2008b), which includes speciﬁcations for the selection of monitoring stations, as well as quality assurance and quality control procedures for data collection and data analysis. The summary statistics were generated using SPSS software version 15, Fig. 1.Location of CAQM stations in Malaysia.

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and the statistical analyses were performed using R software version 2.8.0 (R Development Core Team, 2008).

A generalised additive model (GAM) using a Poisson distribution with a log-link function was adopted to construct the core models.

These were used to regress the daily number of mortalities as the dependent variable. The time variable (day), daily mean temperature, rainfall and humidity, a holiday indicator and the day of the week were the covariates. This Poisson model incorporated natural spline smoothing functions to adjust for long-term seasonality patterns in mortality and other time-varying covariates that might confound the association between air pollution and mortality. The procedure started by developing the basic core model, with adjustments for seasonality, trends and potential confounders. A public holiday indicator was captured using coding values 1 or 0.

Furthermore, the week started from Sunday, which was coded as 1 and included in the core model. By adding terms for meteorology variables such as daily mean temperature, rainfall and humidity, strong confounders of the series that needed to be controlled in the model were managed. All these variables were added to the core model to control systematic variations over time, long-term year trends and short-term temporal variations of the day of the week, in addition to making adjustments for meteorological variables.

The equation below (Eq.(1)) shows the Poisson regression in GAM:

Log½EðyÞ ¼

b

₀þ

b

₁Z₁þ

b

₂Z₂þX

Sð

g

_i;df_iÞ; (1)

where

E(y): expected daily mortality counts, Z₁: dummy variableedays of the week, Z2: dummy variableeholiday indicator

g: covariatesetime, temperature, rainfall and humidity,

b: regression coefﬁcients,

S: smoothing function using natural spline,

df: degree of freedom, with 3dfsfor temperature, humidity and rainfall and 4e6dfsper year for time.

We examined the plot of the partial autocorrelation function (PACF) of the residuals in the core model to minimise autocorrelation, which would bias the standard errors. The aim of the core model was for PACF plots to have coefﬁcients in absolute values

<0.1 for thefirst two lag days. The PACF plots with variousdfswere used to check the adequacy of the core model. The PACF plot for 4 dfsper year fulfilled the requirement ofp<0.1 for thefirst two lag days (Wong et al., 2008a). The randomness of the residuals and the minimum Akaike information criterion (AIC) were also used as criteria for the minimally adequate model. The core model with 4 dfsper year for time was chosen.

The modelling procedure was divided into four stages: 1)fitting the core model that contained only the season and trend elements, 2)fitting the single-pollutant model for each pollutant, 3)fitting multiple-pollutant models and 4) sensitivity analysis, which was carried out at the final stage to measure the consistency of the estimates. Pollutants that were found to be significant in the single- pollutant model (Stage 2) were then combined in the multi pollutant models (Stage 3). The results are discussed in terms of the relative risk (RR) and excess risk (ER). The ER per interquartile range (IQR) of each pollutant increase was calculated as ER¼[exp(IQR*b1)1]*100 or ER¼(RR1)*100. Effects of the pollutants on the current day (lag 0) to the previous seven days (lag 7) were observed. The mean effects of the pollutants on mortality from a few days up to seven days were also determined. Similar procedures were undertaken for the eight days mean of lag 0 to lag 7 (lag 07). Sensitivity analysis was carried out to evaluate the robustness of thefindings to time trends and weatherfluctuations, following the approach undertaken byNuntavarn et al. (2008).

3. Results and discussion

3.1. Descriptive analysis

Table 2 summarises the daily mortality rates, air pollutant concentrations and meteorological conditions for the Klang Valley over the study period. Respiratory mortality (n¼6036) accounted for 12.4% of the natural mortality (n¼48,580). The daily mean and maximum values for all the pollutants were below the Malaysian air quality guidelines (MAQG), except for maximum PM10, which was over three times higher than the MAQG. The overall daily mean of PM₁₀was 58.6223.47m^{g m}³, which exceeds the threshold concentration of the WHO’s air quality guidelines for 2006 of a PM10 value of 50 m^{g m}³. The highest concentration of PM10 Table 2

Summary statistics of daily mortality counts, air pollutants concentrations and meteorological conditions in Klang Valley, 2000e2006.

Variables MeanSD Coefﬁcient of

variation (CV)

Min P25 P50 P75 Max MAQG WHO AQG

2006

Natural mortality (n¼48,580) 195.57 0.29 3 15 19 22 44

Respiratory mortality (n¼6036) 2.361.60 0.68 0 1 2 3 9

Temperature (C) 27.821.11 0.04 23.20 27 27.82 28.60 30.80

Relative humidity (%) 77.905.93 0.08 35 73.90 78.50 82.40 93.60

Rainfall (mm per h) 8.4914.37 1.69 0 0 1.40 11.20 137.30

CO (mg m³) 1563.39445.45 0.29 504.60 1252.75 1551.48 1837.42 6730.70 10,000* 11,500

O3daily mean (m^{g m}³⁾ ^34.0511.48 0.34 2 26 32 40 88 120* 100

O3daily max (mg m³) 111.4135.76 0.32 26.13 85.26 107.80 135.08 257.74 NA NA

SO2(mg m³) 11.933.29 0.28 2.66 10.64 10.64 13.30 26.60 105^þ 20

NO2(mg m³) 37.498.79 0.23 11.46 30.56 36.29 43.93 74.49 320^þ 200

PM10(mg m³) 58.6223.47 0.40 20.41 45.28 54.37 66.65 484.82 150 50

Note: *Averaging time for CO and O3are 8 h,þAveraging time for SO2and NO2are 1 h. Averaging time for PM10is 24 h; NA is not available.

Table 1

The locations of CAQMS in Klang Valley.

Station location Category Parameters Geographic coordinates Gombak Water

Service Department

Residential CO, O3, SO2, NO2, PM10, THC, UV

N03 15.702⁰

E101 39.103⁰ Raja Zarina Secondary

School, Klang

Trafﬁc CO, O3, SO2, NO2, PM10, THC, UV

N03 00.620⁰

E101 24.484⁰ Seri Permaisuri

Secondary School, Cheras

Residential CO, O3, SO2, NO2, PM10, THC

N03 06.376⁰

E101 43.072⁰ Seri Petaling Primary

School, Petaling Jaya

Trafﬁc SO2, NO2, PM10 N03 06.569⁰

E101 38.329⁰ Country Height,

Kajang

N02 59.645⁰

E101 44.417⁰ TTDI Jaya Primary

School, Shah Alam

N03 06.287⁰

E101 33.368⁰ Source: Malaysian Department of Environment (DOE, 2010).

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(484.8m^{g m}³) was recorded in August 2005 and attributed to biomass burning in South East Asia during the dry season, June to October (Anwar et al., 2010). The comparison of the pollutants indicated that PM10 has the highest coefﬁcient of variation over time, followed by O₃, CO, SO₂and NO₂. This variation may be the result of changes in meteorological conditions.

The relationship between daily pollutants’concentrations and daily meteorological levels, as indicated inTable 3, shows that all correlations between the pollutants were positive and highly signiﬁcant, except those between CO and O3. The highest correlation noted was between CO and PM10(r¼0.659,p<0.001), followed by CO and NO₂(r¼0.597,p<0.01). Among the correlations between the pollutants and the meteorological variables, O3and temperature showed the highest positive and signiﬁcant correlation, with anr-value of 0.399. This indicates that as the temperature increased, the concentration of O3also increased. InTable 3, PM10

showed a positive and reasonable correlation with temperature (r¼0.34,p<0.01). The high temperature was predominantly due to an increase in the quantity of biomass burnt and the evaporation of materials such as soil dust from the earth’s surface (Azmi et al., 2010).

The time series of the total number of daily mean temperatures in the Klang Valley between 2000 and 2006 is shown inFig. 2. The results show a yearly seasonal pattern in the temperature series, whichﬂuctuated between 23.2C and 30.8C. High temperatures were observed consistently in May in all years, except from 2005 to 2006. In these two years, the highest daily mean temperature occurred earlier, namely March and April. The southwest monsoon, which occurs annually between May and September, is usually connected to the dry season (Azmi et al., 2010). This is different in climates in temperate countries, where the temperatureﬂuctuates,

for example, between23.9C and 26.2C (Goldberg et al., 2001), 13.2C and 27.0C (Fischer et al., 2011) and 3.7C and 30.4C (Almeida et al., 2011).

The negative significant correlations between PM10 and humidity (r¼ 0.22,p<0.01) and also rainfall (r¼ 0.09,p<0.01) were expected because humidity and rainfall would have reduced the number of particles in the air as a result of wash-out processes, as confirmed byAzmi et al. (2010). The results point to a complex relationship and co-linearity between the air pollutants (Table 3). It was difficult to separate the effect of individual pollutants on mortality.

3.2. Analytical results

3.2.1. Single-pollutant model for natural mortality

The estimated risk for each pollutant was obtained for every IQR increase at different lag times up to seven days. We found signiﬁ- cant associations in the single-pollutant model (Table 4) between PM10and daily mean O3and natural mortality at lag 1 for PM10

(RR¼1.0099, 95% CI¼1.0009e1.0192) and for daily mean O₃at lag 2 (RR¼1.0181, 95% CI¼1.0053e1.0127), lag 02 (RR¼1.0178, 95%

CI¼1.0002e1.0176) and lag 05 (RR¼1.0215, 95% CI¼1.0013e 1.0202). CO was not significantly associated with natural mortality. However, the RRs of CO were consistently higher than PM10in all the lags (except at lag 1), with the maximum at lag 03 (RR¼1.0151; 95% CI¼0.9969e1.0182). Ourfindings indicate that despite the significant results for PM10, the magnitude of the RRs of PM10was not important for natural mortality compared with either the daily mean O₃ or CO. As statistical significance depends on other factors such as the sample size and pollutant variability, the relative magnitude of the risks must be taken into consideration (Thompson, 1998).

The RR increased for PM₁₀, CO and daily mean O₃from lag 0 and reached a maximum concentration at lag 1 for PM10, lag 2 for CO and daily mean O3, before declining to the lowest value at lag 3 (Fig. 3). The RRs at the cumulative mean lags were higher than individual lags for gaseous pollutants compared with PM10. The estimated risks for each pollutant given an increase of 10m^{g m}³^at different lag times are included in theSupplementary appendix.

3.2.2. Two-pollutants model for natural mortality

As stated above (Section3.2.1), PM₁₀and daily mean O₃had the highest RR in the single-pollutant model. These pollutants were then combined in the two-pollutants model for natural mortality.

To avoid co-linearity, PM10at lag 1 wasfitted with daily mean O3at lag 2 in thefirst model, PM₁₀at lag 1 with daily mean O₃at lag 02 in the second model and,finally, PM10at lag 1 with daily mean O3at lag 05 in the third model (Table 5). The daily mean O3at lag 2 days remained the only pollutant with a significant association with natural mortality after controlling for PM10 at lag 1 in the two- pollutants model (RR ¼ 1.0154, 95% CI ¼ 1.0022e1.0288).

Table 3

Pearson correlation coefﬁcients (r) among mortalities and air pollutants in Klang Valley during 2000e2006.

CO O3daily mean O3daily max SO2 NO2 PM10 Temperature Rainfall Humidity

CO 1

O3daily mean 0.002 1

O3daily max 0.159(**) 0.81(**) 1

SO2 0.43(**) 0.09(**) 0.262(**) 1

NO2 0.59(**) 0.28(**) 0.395(**) 0.45(**) 1

PM10 0.66(**) 0.31(**) 0.284(**) 0.29(**) 0.53(**) 1

Temperature 0.04(*) 0.39(**) 0.227(**) 0.16(**) 0.04(*) 0.34(**) 1

Rainfall 0.03 0.05(**) 0.067(*) 0.06(**) 0.07(**) 0.09(**) 0.38(**) 1

Humidity 0.12(**) 0.34(**) 0.115(**) 0.01 0.14(**) 0.21(**) 0.75(**) 0.42(**) 1

** Correlation is signiﬁcant at the 0.01 level (2-tailed).

* Correlation is signiﬁcant at the 0.05 level (2-tailed).

Fig. 2.Daily average temperature plot in Klang Valley between 2000 and 2006.

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However, respiratory mortality was only signiﬁcant in the single- pollutant model.

3.2.3. Single-pollutant model for respiratory mortality

Table 6summarises the results of the single-pollutant models for the estimates of every IQR increase in each pollutant for respiratory mortality. Statistically signiﬁcant associations with respiratory mortality at different lags were found for all the

pollutants, except for SO2. Our ﬁndings showed that the relative magnitude of risks for an association of the pollutants with respiratory mortality was in the order of: daily mean O3, CO and NO2, daily maximum O3and, ﬁnally, PM10. The highest association of each pollutant with mortality was observed with the daily mean of O3at lag 07 (RR¼1.1054; 95% CI¼1.0389e1.1762), CO at lag 05 (RR¼1.0589; 95% CI¼1.0043e1.1165), NO2at lag 2 (RR¼1.0559;

95% CI¼1.0011e1.1137), daily maximum O3at lag 2 (RR¼1.0435, 95% CI¼1.0022e1.0866) and PM₁₀at lag 05 (RR¼1.0363; 95%

CI¼1.0058e1.0678). In other words, thesefindings indicate that daily mean O3showed an important association with respiratory mortality, with over 10% of mortality associated with changes in the IQR. In terms of other significant observations, NO2 at lag 05 (RR ¼ 1.0753; 95% CI ¼ 0.9976e1.1591) and CO at lag 2 (RR¼1.0408; 95% CI¼0.9991e1.0842) showed a relatively high magnitude of risk, despite their insignificant association with mortality. In relation to cardiovascular mortality, no significant association was observed with the pollutants. The results are pre- sented in theSupplementary appendix.

Based on the association with respiratory mortality (Fig. 4), the RR for NO₂showed a slightly higher increase from lag 0 to lag 3 compared with the other pollutants, which RRs were closer to one another. All the pollutants showed similar increasing patterns at longer cumulative mean lags up to lag 05, and gaseous pollutants appeared to show a stronger association with mortality than the PM10. The RR of daily mean O3 increased abruptly and reached a maximum of 1.1054 at lag 07.

In the single-pollutant models, the RR estimates for daily mean O3at lag 2, lag 02 and lag 05 were signiﬁcantly associated with natural mortality, whereas for respiratory mortality, signiﬁcant associations were found at lag 2, lag 02, lag 03, lag 05 and lag 07.

There was an increasing pattern in the RR for respiratory mortality at cumulative mean concentrations of pollutants at various lags (Tables 4e6). Theseﬁndings are in accordance with other studies conducted in Asian cities (Wong et al., 2008a, 2008b) and temperate countries (Fischer et al., 2011;Anderson et al., 2004).

In the single-day lag models (e.g., lag 1 and lag 2), the estimated effects of the pollutants were lower compared with multiday exposure (e.g., lag 01 and lag 03) (Figs. 3and4). These results are consistent withﬁndings from previous studies undertaken in other cities (Bell et al., 2004;Zanobetti et al., 2002). This phenomenon is known as‘mortality displacement’or‘harvesting effects’and refers to short-term increases in air pollution that only affect a pool of individuals who are particularly susceptible. In the event of an increase in the concentrations of pollutants, this group of people is removed from the population at risk either through mortality or hospitalisation, leaving a smaller number of people vulnerable.

Therefore, on subsequent days, lower than expected levels of mortality might be observed (Zanobetti et al., 2000). As

0.990 0.995 1.000 1.005 1.010 1.015 1.020 1.025

0 1 2 3 01 02 03 05 07

Lag Days

PM10 Daily Mean O3 CO

Fig. 3.RR of natural mortality for every IQR changes in air pollutant levels at various lags.

Table 5

RR of natural mortality for every IQR changed of air pollution levels estimated by two-pollutants model.

Model Pollutants (mg m³)

Lag Relative risk (RR) for every IQR increase

Lower 95% CI for RR

Upper 95% CI for RR

%ER p-Value

1 PM10 Lag 1 1.0069 0.9973 1.0165 0.69 0.157

O3 Lag 2 1.0154 1.0022 1.0288 1.54 0.022*

2 PM10 Lag 1 1.0078 0.9980 1.0177 0.78 0.118

O3 Lag 02 1.0126 0.9941 1.0315 1.26 0.184

3 PM10 Lag 1 1.0078 0.9985 1.0172 0.78 0.101

O3 Lag 05 1.0155 0.9943 1.0372 1.55 0.153

Note: All models included time covariate, temperature, humidity, rainfall, day of the week and holiday indicator.

* Signiﬁcant at the 0.05 level (2-tailed).

** Signiﬁcant at the 0.01 level (2-tailed).

Table 4

RR of natural mortality as a function of selected ambient pollutant levels for single pollutant model.

Pollutant (mg m³)

Lag RR for every IQR increase

Lower CI for RR

Upper CI for RR

%ER p-Value

PM10 0 1.0037 0.9942 1.0134 0.37 0.447

1 1.0099 1.0009 1.0192 0.99 0.032*

2 1.0078 0.9987 1.0169 0.78 0.094

3 1.0024 0.9932 1.0118 0.24 0.604

01 1.0079 0.9979 1.0179 0.79 0.121

02 1.0091 0.9987 1.0196 0.91 0.086

03 1.0084 0.9975 1.0193 0.84 0.133

05 1.0054 0.9941 1.0168 0.54 0.349

07 1.0027 0.9905 1.0149 0.27 0.667

CO 0 1.0083 0.9938 1.0146 0.83 0.264

1 1.0094 0.9952 1.0143 0.94 0.196

2 1.0109 0.9968 1.0142 1.09 0.129

3 1.0084 0.9942 1.0143 0.84 0.245

01 1.0109 0.9949 1.0159 1.09 0.182

02 1.0138 0.9967 1.0171 1.38 0.116

03 1.0151 0.9969 1.0182 1.51 0.102

05 1.0109 0.9916 1.0194 1.09 0.270

07 1.0136 0.9927 1.0211 1.36 0.205

O3daily mean 0 1.0037 0.9896 1.0143 0.37 0.610

1 1.0079 0.9951 1.0129 0.80 0.290

2 1.0181 1.0053 1.0127 1.81 0.005**

3 1.0038 0.9912 1.0127 0.38 0.560

01 1.0079 0.9918 1.0163 0.80 0.336

02 1.0178 1.0002 1.0176 1.78 0.048*

03 1.0173 0.9986 1.0188 1.73 0.071

05 1.0215 1.0013 1.0202 2.15 0.038*

07 1.0172 0.9952 1.0222 1.72 0.126

O3daily max 0 1.0036 0.9883 1.0154 0.36 0.650

1 1.0015 0.9868 1.0149 0.15 0.842

2 1.0146 0.9999 1.0146 1.46 0.051

3 1.0049 0.9904 1.0147 0.49 0.511

01 1.0040 0.9851 1.0192 0.40 0.680

02 1.0139 0.9926 1.0216 1.40 0.203

03 1.0159 0.9924 1.0236 1.59 0.187

05 1.0195 0.9932 1.0264 1.95 0.147

07 1.0183 0.9891 1.0295 1.83 0.222

Results of other pollutants (NO2and SO2) are given in theSupplementary.

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a consequence of this, in the literature, the mean concentrations of several days have been used to examine the relationship between air pollution and mortality (Nuntavarn et al., 2010;Wong et al., 2010).

3.3. Effects of O3on mortality

In the single-pollutant model, the independent health effect of daily mean O3 was higher for respiratory mortality, with every increase in IQR associated with a 10.54% increase in respiratory mortality compared with natural mortality (2.15%). Thisﬁnding, more or less, concurs with other studies (Nuntavarn et al., 2010;

Wong et al., 2010;Fischer et al., 2011). There is a biological plau- sibility for the respiratory effect of O₃, where it is possible for it to generate a harmful reaction in the human upper respiratory tract and lungs, even at very low levels. Increased airway inflammation and deterioration in pulmonary function and gas exchange are among the health effects of O3, as reported in laboratory studies (Mudway and Kelly, 2004; Brown et al., 2008). A cohort study undertaken in the U.S. byJerrett and Burnett (2009)revealed that an increasing rate of mortality due to respiratory diseases corre- sponded with a rise in the number of people exposed to O3through various activities such as working and jogging. Thisfinding could also represent a combination of short-term effects O₃may have on people who already have, for example, influenza, pneumonia, asthma or any underlying respiratory disease that makes them more susceptible. O₃could also accelerate the natural loss of adult lung function with age, such that those people who are exposed to a higher concentration of O3have a greater likelihood of dying (Jerrett and Burnett, 2009).

Our analysis revealed associations between O3and daily natural mortality and between O3 and daily respiratory mortality. These results add weight to consistent associations between daily mortality and gaseous pollutants previously reported in Asian cities (Yang et al., 2004;Wong et al., 2008b). Photochemical interactions of emitted pollutants (NOxand VOCs) that are predominantly due to the high intensity of sunlight could lead to high O₃concentrations (Ghazali et al., 2010;Latif et al., 2012.).

0.920 0.940 0.960 0.980 1.000 1.020 1.040 1.060 1.080 1.100 1.120

0 1 2 3 01 02 03 05 07

Lag Days Daily mean O3 NO2

CO Daily max O3

PM10

Fig. 4.RR of respiratory mortality for every IQR changes in air pollutant levels at various lags.

Table 7

Percent ER in natural mortality and respiratory mortality for IQR increase using variousdfof time.

Mortality Dfof time p-Value ER (%)

Natural 4^a 0.005 1.81

5 0.008 1.74

6 0.013 1.66

7 0.007 1.79

8 0.007 1.81

9 0.002 2.05

15 0.002 2.13

4 with temperature at lag 3 to 7 0.002 2.01

Respiratory 4^a 0.011 4.55

5 0.031 3.89

6 0.082 3.18^b

7 0.094 3.09^b

8 0.099 3.05^b

9 0.090 3.16^b

12 0.047 3.76

15 0.138 2.85^b

4 with temperature at lag 3 to 7 0.014 4.48

aMain analysis of single pollutant model with daily mean O3at lag 2 and 4dfsfor time.

bER>20% from the main analysis.

Table 6

a) RR of respiratory mortality as a function of PM10and CO levels in a single pollutant model. b) RR of respiratory mortality as a function of O3daily mean and O3daily maximum levels in a single pollutant model. c) RR of respiratory mortality as a function of NO2daily mean levels in a single pollutant model.

Pollutant (mg m³)

Lag and mean cumulative lags

Relative risk (RR) for every IQR increase

Lower 95% CI for RR

Upper 95% CI for RR

%ER p-Value

a

PM10 0 1.0143 0.9874 1.0420 1.43 0.300

1 1.0189 0.9929 1.0458 1.89 0.155

2 1.0321 1.0072 1.0577 3.21 0.011*

3 1.0318 1.0072 1.0569 3.18 0.011*

01 1.0189 0.9907 1.0478 1.89 0.191

02 1.0279 0.9987 1.0581 2.79 0.061

03 1.0346 1.0041 1.0660 3.46 0.026*

05 1.0363 1.0058 1.0678 3.63 0.019*

07 1.0289 0.9956 1.0632 2.89 0.089

CO 0 1.0296 0.9869 1.0741 2.96 0.177

1 1.0248 0.9829 1.0685 2.48 0.250

2 1.0408 0.9991 1.0842 4.08 0.055

3 1.0383 0.9969 1.0815 3.83 0.071

01 1.0331 0.9864 1.0821 3.31 0.167

02 1.0452 0.9948 1.0980 4.52 0.079

03 1.0541 1.0005 1.1105 5.41 0.048*

05 1.0589 1.0043 1.1165 5.89 0.034*

07 1.0511 0.9918 1.1138 5.11 0.093

b O3daily

mean

0 1.0189 0.9769 1.0627 1.89 0.383

1 1.0239 0.9855 1.0639 2.39 0.225

2 1.0455 1.0071 1.0854 4.55 0.019*

3 1.0321 0.9941 1.0716 3.21 0.098

01 1.0314 0.9831 1.0819 3.14 0.207

02 1.0537 1.0006 1.1096 5.37 0.047*

03 1.0641 1.0069 1.1245 6.41 0.028*

05 1.0942 1.0338 1.1581 9.42 0.002**

07 1.1054 1.0389 1.1762 10.54 0.002**

O3daily maximum

0 1.0019 0.9601 1.0456 0.19 0.929

1 1.0166 0.9757 1.0592 1.66 0.433

2 1.0435 1.0022 1.0866 4.35 0.039*

3 1.0370 0.9959 1.0798 3.70 0.078

01 1.0151 0.9630 1.0699 1.51 0.577

02 1.0438 0.9838 1.1074 4.37 0.156

03 1.0637 0.9969 1.1349 6.37 0.062

05 1.0092 0.9376 1.0862 0.92 0.015

07 1.0093 0.9299 1.0954 0.92 0.028

c

NO2 0 0.9936 0.9396 1.0508 0.64 0.823

1 1.0098 0.9568 1.0658 0.98 0.722

2 1.0559 1.0011 1.1137 5.59 0.046*

3 1.0512 0.9969 1.1086 5.12 0.065

01 1.0023 0.9416 1.0668 0.23 0.943

02 1.0308 0.9632 1.1031 3.08 0.38

03 1.0505 0.9769 1.1295 5.05 0.183

05 1.0753 0.9976 1.1591 7.53 0.058

07 0.9936 0.9396 1.0508 0.64 0.104

Results of SO2are given in theSupplementary.

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3.4. Effects of CO and NO2on mortality

Our results indicated that exposure to current concentrations of CO are associated with natural and respiratory mortality. Every IQR increase in exposure to CO was associated with maximum increases of 1.51% in natural mortality and 5.89% in respiratory mortality.

Among ambient pollutants, CO is known for its biological toxicity: it can combine with haemoglobin in the lungs to form carbox- yhemoglobin (COHb). COHb results in a decrease in the oxygen- carrying capacity of the blood, reducing the availability of oxygen to body tissues and resulting in tissue hypoxia (Tao et al., 2011).

Resulting health effects are impaired vision and coordination,

a

b

c

Fig. 5.Comparing ER from the worldwide studies on O3, CO and PM10with natural mortality.

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headaches, dizziness, confusion and nausea at low exposure and fatal effects at high exposure (Raub and Benignus, 2002).

Our analyses estimated that exposure to NO2was associated only with respiratory mortality, with every increase in IQR associated with a 7.53% increase in respiratory mortality. NO_xcan be used as a tracer of road trafﬁc emissions at monitoring sites located in urban areas (Lewne et al., 2004). NO2 is mainly a secondary pollutant because in most ambient situations NO is emitted and transformed into NO2 in the atmosphere through photochemical processes (Chaloulakou et al., 2008). People suffering from respiratory diseases such as asthma are very sensitive to NO₂at high concentrations. Some studies have shown that both short- and long-term exposure to NO2 can induce effects on human health, given the role of NO₂as a precursor of other pollutants and as a marker for trafﬁc-related pollution (European Commission, 1997;WHO, 2006).

3.5. Effects of PM10on mortality

Our results indicated that every IQR increase in exposure to PM10was associated with a maximum increase of 0.99% in natural mortality and a 3.63% increase in respiratory mortality. Exposure to elevated levels of particulates has been associated with health effects in many epidemiological studies in temperate countries, as well as in tropical countries (Zhou et al., 2011;Wong et al., 2001;

Nuntavarn et al., 2008). Several recent studies conducted in North America and Europe have analysed the effects of air pollution on mortality by season and have provided important evidence that the effect of air pollution depends on temperatures (Yi et al., 2010).

3.6. Sensitivity analysis

The daily mean of O3was chosen as the pollutant in the sensitivity analysis because it produced the highest ER in the single- pollutant model, as shown inTable 4. A change in the ER of>20%

from the analysis in the single-pollutant model for O3was used as an indication of the sensitivity of the data (Wong et al., 2008b). The models examined were from 3 to 15dfsper year for time.Table 7 summarises the results of the sensitivity analysis, with a focus on the daily mean of O3at lag 2 for natural mortality and the daily mean of O₃at lag 03 for respiratory mortality. The results point to effects on the ER for differentdfsin the smoothing of time, as well as for the different lags of temperature.

The results showed that the ER in natural mortality was generally insensitive to the number ofdfsspeciﬁed for time and adjusted for temperature (Table 7). The results for respiratory mortality were slightly sensitive to the variousdfsof time based on the ER of >20% from the main analysis in the single-pollutant model for daily mean O3. This might well be due to the low number of daily respiratory-associated mortalities in the Klang Valley between 2000 and 2006. It can be concluded that the results of the sensitivity analysis are robust and consistent, even with the various changes made to thedfs.

3.7. Comparison with other studies

Fig. 5(a)e(c) show the percentage of ER from natural mortality for every IQR increase daily mean O₃, CO and PM₁₀respectively. The ER in our study on the effects of daily mean O3on natural mortality Fig. 5(a) was slightly higher compared with studies byRen et al.

(2010) in Massachusetts, Gouveia and Fletcher (2000) in Sao Paulo andYang et al. (2004)in Taipei, but lower than the study by Goldberg et al. (2001)in Quebec. In relation to CO, the ER in our study was the highest compared with that reported in other studies byGouveia and Fletcher (2000),Yang et al. (2004)andBreitner et al. (2009)in Erfurt, Germany (Fig. 5(b)).

As shown inFig. 5(c), the ER of PM10in our study was higher than that reported by Gouveia and Fletcher (2000), Yang et al.

(2004) and Breitner et al. (2009), but lower than reported by Mallone et al. (2011)in Rome. The estimates obtained in this study for O₃, CO or PM₁₀have smaller CIs compared with other studies, indicating better precision. The magnitude of the associations and the percentage of ER between the daily mean O3, CO and PM10and daily natural mortality are comparable to those reported internationally.

4. Conclusions

Our findings show that natural mortality was associated predominantly with daily mean O3, followed by CO and PM10. For respiratory mortality, the relative magnitude of the risks for the association of the pollutants with respiratory mortality was in the order: daily mean O3, CO and NO2, daily maximum O3and,finally PM10. Our results demonstrate that all the pollutants, except SO2, were significantly associated with natural mortality and with respiratory mortality in the general population in the Klang Valley, even at levels below the MAQG. Higher risks of mortality due to exposure to the pollutants were found for respiratory mortality compared with natural mortality, and gaseous pollutants were more harmful compared with particulates. Our findings indicate that effective air quality management and strategies are needed to reduce the concentrations of pollutants, particularly O₃.

Acknowledgements

The authors would like to gratefully acknowledge the Institute for Medical Research (IMR), the National Institute for the Health Grant (JPP-IMR No: NONCAM-07-014), Universiti Kebangsaan Malaysia for the Research Grant (UKM-NN-03-FRGS0041-2010) and the Malaysian government agencies for providing data: the Department of the Environment (DOE), the Department of Statistics (DOS) and the Meteorological Department. We also wish to thank the Director General of Health, Ministry of Health, Malaysia and the Director of the IMR for the permission to publish this paper. The authors would like to extend appreciation to Dr Amal Nasir Mus- taffa and Dr Ahmad Faudzi Yusoff from the Medical Research Resource Center, IMR and Prof. Dr Chang Chuan Chan from College of Public Health, National Taiwan University, Taiwan for their continued support with this study.

Appendix A. Supplementary data

Supplementary data related to this article can be found athttp://

dx.doi.org/10.1016/j.atmosenv.2012.10.019.

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