Risk assessment for cardiovascular and respiratory mortality due to air pollution and synoptic meteorology in 10 Canadian cities

Environmental Pollution 185 (2014) 322e332

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Risk assessment for cardiovascular and respiratory mortality due to air pollution and synoptic meteorology in 10 Canadian citiesq Jennifer K. Vanos a, b, Christopher Hebbern a, Sabit Cakmak a, * a b

Health Canada, Environmental Health Science and Research Bureau, Population Studies Division, 50 Columbine Driveway, Ottawa, ON K1A 0K9, Canada Department of Geosciences, Texas Tech University, 2500 Broadway, St. Lubbock, TX 79401, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 July 2013 Received in revised form 7 October 2013 Accepted 10 November 2013

Synoptic weather and ambient air quality synergistically influence human health. We report the relative risk of mortality from all non-accidental, respiratory-, and cardiovascular-related causes, associated with exposure to four air pollutants, by weather type and season, in 10 major Canadian cities for 1981 through 1999. We conducted this multi-city time-series study using Poisson generalized linear models stratified by season and each of six distinctive synoptic weather types. Statistically significant relationships of mortality due to short-term exposure to carbon monoxide, nitrogen dioxide, sulphur dioxide, and ozone were found, with significant modifications of risk by weather type, season, and mortality cause. In total, 61% of the respiratory-related mortality relative risk estimates were significantly higher than for cardiovascular-related mortality. The combined effect of weather and air pollution is greatest when tropical-type weather is present in the spring or summer. Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved.

Keywords: Mortality Respiratory Cardiovascular Relative risk Spatial synoptic classification Air pollution

1. Introduction Weather and ambient air pollution are known influences on human mortality and morbidity, but their covariance results in a high likelihood that the effect of one modifies the effect of the other. Due to these and other potential confounding factors, health risk models for air pollution often include weather-related variables as controls (Anderson et al., 2001; Cakmak et al., 2006; Dales et al., 2006; Roberts, 2004; Zanobetti and Schwartz, 2005). More recently, studies examining acute health endpoints have reported the simultaneous effects of both weather variables and air pollutants on health. Many temperatureemortality studies fail to account for air pollution when modelling (Basu and Samet, 2002), which may cause the models to overestimate mortality associated with weather (Rainham and Smoyer-Tomic, 2003). Much of the environmental epidemiological literature associates temperature with mortality, and uses air pollution as a confounder, an effect modifier, or both (Basu, 2009). Other variables, such as additional meteorological attributes, time trends, and air pollution interactions, have

q This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike License, which permits noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited. * Corresponding author. E-mail address: [email protected] (S. Cakmak).

also been investigated as effect modifiers or confounders (Filleul et al., 2006; Ren and Tong, 2006; Ren et al., 2006; Smoyer-Tomic et al., 2003), yet teasing apart the independent effect of each is difficult (Basu, 2009). For example, in a study examining deaths in nine cities during the 2003 heat wave in France (Filleul et al., 2006), ozone and temperature each made a statistically significant contribution to the number of daily deaths observed. Studies have controlled for weather using synoptic weather type-based approaches (Cheng et al., 2009; Hanna et al., 2011; Rainham et al., 2005; Samet et al., 1998; Vaneckova et al., 2008). Cheng et al. (2009) used synoptic weather typing to differentiate between the combined impacts of extreme temperatures and air pollution on human mortality in five south-central Canadian cities. On extreme weather and air pollution days, the annual mean mortality was significantly above baseline (e.g., Ottawa ¼ 462 (CI 438e486)); however, 80% of this mortality was attributable to air pollution. Further, seasonal risk estimates of mortality have been shown to differ yearly, with statistically significant associations between airborne particles and daily death for both summer and winter seasons in 10 U.S. cities with varying climates (Schwartz, 2000). The risks of mortality differ when associating a specific cause of death to the ambient environment, yet there are relatively few studies (Choi et al., 1997; Hales and Salmond, 2000; Ren and Tong, 2006; Ren et al., 2007, 2006) that examine cause-specific mortality. Ren et al. (2006) found that airborne particles significantly

0269-7491/$ e see front matter Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.envpol.2013.11.007

J.K. Vanos et al. / Environmental Pollution 185 (2014) 322e332

modified the effects of air temperature on respiratory and cardiovascular hospital admissions in Brisbane, Australia. Ozone alone has been associated with higher levels of hospital admissions, respiratory symptoms, impaired lung development, and mortality, among other adverse health responses (Anderson et al., 2004; Bell et al., 2006; Dockery and Pope, 1994; Hanna et al., 2011; Levy et al., 2005; Stieb et al., 2003). Increasing sunlight and temperatures also increase tropospheric ozone production due to the photochemical nature of this secondary pollutant. According to HYPERLINKBell et al. (2007a,b), projections of a warming climate will add to the current health burden caused by ozone, which is enhanced on hot days. Ozone has been found to be closely associated with further weather variables (Bell et al., 2006; Davis et al., 2010; Hanna et al., 2011), with lower ozone levels found during moderate or cool weather, as well as moist (USEPA, 2004). During summer, ozone has been shown to modify the human health effects of temperature in various regions of the United States (Ren et al., 2006). Stable, dry, and hot conditions result in the greatest pollution build up and ozone production, as abundant sunlight and heat is present for photochemical reactions (Davis et al., 2010). The environmental and health effects of air pollutants are extensive as demonstrated by hundreds of published research papers in many areas of the world (see Confalonieri et al. (2007) and Sujaritpong et al. (2013) for extensive reviews). Some key negative health outcomes due to increased exposures to air pollution include elevated mortality with PM10 (Ren and Tong, 2006; Stafoggia et al., 2008), increases in hospital admissions due to aeroallergens (Dales et al., 2004) and gaseous pollutants (Dales et al., 2006), and increased morbidity (asthma and myocardial infarction) with ozone on hot, dry days (Hanna et al., 2011). A study specific to Canada (Burnett et al., 1998a) found statistically significant positive associations between daily fluctuations in mortality and ambient levels of carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), coefficient of haze, total suspended particulates (TSP), sulphates, and estimated particulates in Toronto (1980e1994). Burnett et al. (1998b) emphasized that all air pollution from vehicular sources, and CO in particular, should be considered as a potential cause of increased mortality in urban populations in Canada. However, there are inconclusive results from many studies, leaving the magnitude of mortality effect modifications due to air pollution or weather type unclear (Basu, 2009; Rainham and Smoyer-Tomic, 2003). Accordingly, the goal of this study is to examine the effect modification of synoptic weather types on the relative risk of mortality (RR) associated with four air pollutants: CO, NO2, O3, and SO2. We subdivide the analysis into all nonaccidental, respiratory-, and cardiovascular-related mortality, and combine the results of 10 Canadian cities to estimate an overall countrywide effect of each pollutant on each mortality type, for the period 1981e1999. A spatial synoptic climatological procedure is used to develop a daily series of prevailing air masses (or surface weather types). The risk of mortality due to exposure to each air pollutant is determined for each weather type. Additionally, seasonality has been found to explain a significant amount of variability in pollution and weather-related mortality studies (Pope and Kalkstein, 1996; Sheridan and Kalkstein, 2010); hence, we further segregate the analysis by season. 2. Methods 2.1. Spatial synoptic classification The current study accounts for weather modification through identifying weather types using the Spatial Synoptic Classification (SSC) system (Sheridan, 2002). The SSC identifies a daily weather type based on four diurnal surface measurements of six meteorological parameters. The weather types include: dry moderate (DM), dry polar (DP), dry tropical (DT), moist moderate (MM), moist polar

323

(MP), moist tropical (MT), moist tropical plus (MTþ), and a transitional (TR) category. The MTþ designation is an extreme subset of MT, in which the morning and afternoon apparent temperatures are above the corresponding MT means for the given location (Sheridan, 2002). Detailed descriptions relevant to Canada can be found in Vanos and Cakmak (2013). The SSC has been implemented in many human health-climate studies (Hajat and Kosatky, 2010; Hanna et al., 2011; Rainham et al., 2005; Sheridan et al., 2009). The SSC accounts for relative temporal and spatial variability, important in Canadian cites due to their wide-ranging climate regimes. For example, spatially, a summer DT day in Toronto is warmer than a summer DT day in Vancouver. Temporally, July MT air in Toronto is warmer than in March (e.g., 28 versus 15  C for Toronto, respectively).

2.2. Mortality and air pollution data Daily mortality and air pollution data from 1981 through 1999 were obtained for ten cities across Canada: Saint John on the east coast; Toronto, Montreal, Ottawa, Windsor, and Quebec City in the Great Lakes/St Lawrence river region; Calgary, Edmonton, and Winnipeg in the Prairies, and Vancouver on the west coast. Mortality data were accessed from the Canadian Institute for Health Information (CIHI) database, including all non-accidental (ICD9 < 800) deaths. National Air Pollution Surveillance (NAPS) data were provided by Environment Canada, consisting of mean daily ambient concentrations of carbon monoxide (CO, ppm), nitrogen dioxide (NO2, ppb), sulphur dioxide (SO2, ppb) and ground-level ozone (O3, ppb) measured either downtown or at city airports located within 27 km of downtown. Hourly weather data from city airports were downloaded from the National Climate Data and Information Archive. Particulate matter (PM10 and PM2.5) was not used in this study due to brief data records in Canada.

2.3. Time-Series modelling A piece-wise Poisson general linear model (GLM) was applied to empirical air pollution measurements of CO, NO2, O3, and SO2 and daily all non-accidental, respiratory, and cardiovascular mortality counts, assuming a linear association between air pollutants and mortality on the logarithmic scale varying at random between cities. Analysis was completed for full year and by season, stratified by weather type. Each time-series model for each city included indicator variables for the day of week, and was adjusted for temporal trends using natural spline functions for day of study, with a knot for each of 30, 90, 180, 270, and 365 days of observation. The optimal model was selected based on the number of knots that either minimized the Akaike’s Information Criteria (AIC) (representing enhanced model strength), or minimized evidence that the model residuals demonstrate departures from the model assumptions, or structure that may not be accounted for in the model. The latter was examined after fitting models with natural spline functions to test for serial correlation in the residuals, which was completed using Bartlett’s test (see Supplementary Figures for an example of a residual plot). The above steps were implemented separately for each city. Finally, each air pollutant was added to the model containing natural splines and indicator variables. Lagging times of 0, 1, 2, 3, 4, and 5 days were examined for the air pollutants, selecting the lag that optimized the effect size. Once the final model was selected, the confidence intervals (CI) for RR were generated across each weather type for each city. The city-specific estimates were pooled using a random-effects model to calculate the overall influence of air pollution on mortality. This approach weights the effect estimates by the inverse sum of within- and betweencity variance, thus accounting for any heterogeneity among the cities in the pooled effect estimates. It also gives greater statistical power, and a final estimation of an overall countrywide effect. The model is summarized as follows: LogEðYt =Xt Þ ¼ bXtl þ DOWt þ ns ðtime; df Þ

(1)

where Yt is the daily count of cause-specific mortality; b is the regression coefficient linking pollution to daily mortality; Xt-l is the pollution level on day t with 0e5 days of lag; DOWt indicates the day of the week on day t; ns(time, df) is the natural spline of calendar time with degrees of freedom corresponding to a knot at 30, 90, 180, 270, and 365 days of observation. The effect estimates for each season and weather type were obtained by pollutant  season/weather type interaction terms. For example, season specific effect estimates can be found by replacing b by bWIW þ bspIsp þ bauIau þ bsuIsu, where Iw, Isp, Iau, and Isu are indicators of winter, spring, autumn and summer respectively. The model also allows for temporal trends to be changed by season by replacing ns(time, df) by nsðtime; df Þ ¼ nsðtime; df ÞIW þ nsðtime; df ÞIsp þ &.g

(2)

Pooled relative risk estimates were obtained for the concentration of air pollutant equivalent to the population weighted mean (PWM) level for all cities in each weather type, and their 95% confidence intervals (CI). Details of the above statistical analyses are given in Cakmak et al. (2006). Significance testing for models was completed using t-tests (p < 0.001). All statistical analyses were completed using R 2.10.1 (The R Foundation for Statistical Computing, 2008).

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J.K. Vanos et al. / Environmental Pollution 185 (2014) 322e332

Table 1 Weather type frequencies and average ten-city mean yearly descriptive statistics by weather type for air pollution concentrations, population weighted mean (PWM) for each cause of morality, and air temperature (1981e1999).

Frequency (%) Air Temperature ( C)

Air Pollution CO (ppm) NO2 (ppb) SO2 (ppb) O3 (ppb) Mortality All-Cause Cardiovascular Respiratory

Dry

Dry

Dry

Moist

Moist

Moist

Moderate

Polar

Tropical

Moderate

Polar

Tropical

23.3 9.7

25.1 1.4

1.8 15.6

16.7 9.4

18.0 3.8

4.1 14.6

10.9 5.9

e 8.9  5.4

Mean  SD

Mean  SD

Mean  SD

Mean  SD

Mean  SD

Mean  SD

Mean  SD

Mean  SD

1.0  21.8  5.2  15.6^

1.0 25.0* 6.0* 30.3*

1.0  21.1  5.0  15.3^

0.9  19.8^ 5.0  13.8^

1.0 22.0 5.0 23.0*

0.8^ 19.5^ 4.8^ 17.8 

0.9 21.1 5.1 19.3

0.9 22.1 5.4 19.0

   

0.26 5.78 2.86 2.39

18.1  0.09 7.5  0.05 1.5  0.02

0.29 5.76 2.29 2.98

18.7  0.20 8.0  0.10 1.7  0.05

   

0.31 6.02 3.68 5.03

17.5  0.44 7.3  0.26 1.5  0.12

0.27 5.56 2.79 3.29

18.5  0.11 7.8  0.06 1.7  0.03

0.24 5.25 2.69 4.08

18.9  0.14 8.0  0.07 1.7  0.04

Transition

   

Mean All weather types

0.35 6.42 2.83 4.76

18.4  0.63 7.4  0.30 1.6  0.23

0.23 5.12 2.28 2.70

19.0  0.18 8.0  0.10 1.7  0.05

   

0.08 1.14 0.21 5.73

18.4  0.50 7.7  0.31 1.6  0.10

NO2

Mean Concentration NO2, O3 (ppb)

50

O3

SO2

CO

10

45

9

40

8

35

7

30

6

25

5

20

4

15

3

10

2

5

1 0

0 J

F

M

A

M

J J Month

A

S

O

N

Mean Concentration SO2 (ppb), CO (ppm)

*Indicates values significantly greater than the all-weather type mean for given variable, as shown in the far right column of mean values, for all air pollutants and all nonaccidental mortality each season (p < 0.05). ^Indicates values as significantly lower than the all-weather type mean for given variable.

D

Fig. 1. Average mean monthly concentrations of four air pollutants (left-axis: NO2 and O3, in ppb); right-axis: SO2 (ppb) and CO (ppm)), for 10 Canadian cities for the period 1981e 1999 inclusive.

Mortality (PWM)

25

Winter

Spring

20 15 10 5

Mortaity (PWM)

0 25

Summer

Fall

20 15 10 5 0 DM

DP

DT

All Non-Accidental

MM

MP

Cardiovascular

MT

TR

DM

DP

DT

MM

MP

MT

TR

Respiratory

Fig. 2. Average mortality for 10 Canadian cities calculated by population weighted mean (PWM), depicting all-non-accidental, cardiovascular, and respiratory mortality.

J.K. Vanos et al. / Environmental Pollution 185 (2014) 322e332

325

Table 2 All weather-typea mean relative mortality risk (RR) estimates at population-weighted mean, pooled across ten cities, with standard deviation (SD) for four pollutants (1981e 1999). CO

Winter Cardiovascular Respiratory Non-accidental Spring Cardiovascular Respiratory Non-accidental Summer Cardiovascular Respiratory Non-accidental Fall Cardiovascular Respiratory Non-accidental a

NO2

SO2

O3

RR

SD

RR

SD

RR

SD

RR

SD

1.034 1.065 1.016

0.009 0.010 0.018

1.032 1.074 1.020

0.007 0.025 0.012

1.022 1.069 1.023

0.014 0.011 0.012

1.048 1.067 1.029

0.016 0.026 0.015

1.061 1.132 1.037

0.008 0.055 0.013

1.052 1.118 1.040

0.020 0.028 0.015

1.026 1.068 1.022

0.018 0.048 0.023

1.034 1.093 1.038

0.016 0.034 0.024

1.058 1.134 1.043

0.018 0.074 0.021

1.045 1.103 1.043

0.006 0.034 0.018

1.043 1.066 1.032

0.035 0.046 0.032

1.060 1.083 1.033

0.020 0.019 0.008

1.035 1.063 1.024

0.011 0.019 0.007

1.043 1.087 1.038

0.019 0.017 0.008

1.032 1.066 1.039

0.006 0.016 0.005

1.021 1.065 1.015

0.010 0.015 0.006

This excludes the transitional days.

3. Results 3.1. Atmospheric conditions and mortality by weather type The dry polar (DP) and dry moderate (DM) weather types are the most frequent in the summer for all cities combined (i.e., 25.1% and 23.3%, respectively (Table 1)). In the hot and dry weather type DT, concentrations of NO2, SO2, and O3 are significantly greater than the all-weather type mean, as are the O3 levels in the MT weather type. Significantly lower concentrations of O3 are present in the DP, MM, and MP weather types, as well as NO2 in the MP weather type. Of the four pollutants, CO levels demonstrate the least change in average monthly concentrations for 1981e1999 (Fig. 1), and the least change by season. Ozone and NO2 display the largest concentration range relative to the yearly mean between

seasons (17 ppb), with O3 demonstrating the highest concentrations in the warm season, and NO2 and SO2 doing so in the cold season. The seasonal PWM of all non-accidental mortality for all cities (Fig. 2) is the highest in the winter season (20.6 deaths per day). Similarly, mean cardiovascular- and respiratory-related mortality is highest in the winter season (8.7 and 2.1 deaths per day, respectively). Summertime DP and MP weather types are associated with significantly lower mortality than the all-weather type mean. The highest mean mortality when divided into weather type occurs in winter, coinciding with the highest concentrations of CO, NO2, and SO2. High springtime mortality coincides with significantly high O3 concentrations, relative to the seasons. City-specific pollution, climate, and mortality information is provided in the Supplementary Tables.

Table 3 Weather types categorized based on increased risk of causing respiratory or cardiovascular morality due to the four air pollutants for four seasons. Categories are relative to current study results (low < 5% increase in RR < medium 5e10%, high>10%). Weather types defined as oppressive in Canada and linked to heat-related mortality are bolded. Season

Highest average RR

Low (<5% increase)

Medium (5e10% increase)

CO

NO2

SO2

O3

Dry Moderate Dry Tropical

DM, DP MM, MP MM

DM, DP MM, MP DP MM, MT

Summer

Dry Tropical

DM, MT

DP MM, MP DM, DP MM, MP MT DM, MM, MP

Fall

NA

DM, DP, MM, MP

DM, DP, DT MM, MP DM, DP, MM, MP

DM, DP MM, MP DM, DP MM, MP MT DM, DP MM, MP MT DM, DP, MM, MP

CO

NO2

SO2

High (>10% increase) O3

CO

NO2

SO2

O3

CVD Winter Spring

Respiratory Winter Dry Moderate Spring Dry Tropical, Moist Tropical Summer Moist Polar Fall Dry Polar

DM, DP, DT MP, MT DP, DT MM, MP

DM, DT MP

DT

DT

MT

DT

DP, DT

DM, DP, MM, MP MM

DP, MP

MT

DM, DP, MM, MP MT MM

DM, DP, MP

DM

DM, DP, MM, MP DM, MP

MT, MM

DM, MM

MP

DM, DP, MM, MP

DM, MP DM, DP

DM, MM, MT DM, MM, MP

DM, MM DM, DP, MM

DM

DP, MM, MP

DP, DT MM, MT

DP, DT MM, MT

DT, MT

DT, MT

DM, DP, MM, MT DM, DP, MM, MP

DP, MP, MT

DP, MT

MP

MP

DP

326

J.K. Vanos et al. / Environmental Pollution 185 (2014) 322e332

3.2. Relative risks of mortality due to air pollution

3.3. Respiratory-related mortality

We report statistically significant relationships between mortality and short-term exposure to air pollution for all nonaccidental, respiratory-, and cardiovascular-related mortality. Exposure to the four air pollutants individually (CO, NO2, SO2, O3) results in overall significantly higher relative risk (RR) estimates for respiratory-related mortality than for cardiovascular mortality, with 61% of the estimates significantly greater due to respiratory mortality This is also 5e10% greater than the all non-accidental mortality estimates, as depicted in the averages in Table 2. All non-accidental mortality demonstrates the highest PWM mortality (Fig. 2); however, the corresponding RR estimates are lower than that of respiratory-related deaths. Cardiovascular-related mortality RR estimates are not significantly different to that of all nonaccidental mortality, consistently within 3% difference due to any air pollutant. Table 3 summarizes the RR estimates for both causes of death (respiratory or CVD) due to exposure to each pollutant in each season and weather type. Relative risk estimates are grouped into ‘low’ (<5% increase in RR), ‘medium’ (5e10% increase) and ‘high’ risks (>10% increase). The warmest weather types in each season consistently present the highest risks for each mortality cause (i.e., DM in winter, DT in summer, MT in spring and fall), although the highest risk of respiratory mortality, and the highest overall increase in RR for both mortality causes, is found within the MP weather type (see Table 4).

Table 2 displays the all-weather type average RR estimates for each season, while Figs. 3e6 display each combined model estimate (and 95% CI) by air pollutant, season, and weather type. All relative risk estimates for respiratory disease are significantly above 1.0, for all combinations of season, pollutant, and weather type. Further, the average yearly respiratory-related RR (not shown) demonstrates the strongest associations with CO and NO2 for all weather types combined, with a 10.7% and 13.3% increase, respectively. The highest RR estimates are found in the springtime, with NO2 and CO exposure resulting in the greatest overall risk (Figs. 3 and 4). The most harmful springtime air pollutant is CO, associated with a 10.0e21.0% increase in mortality for all weather types excluding DM and MP. Although lower in magnitude, the DM and MP relative risk estimates are also significant, and demonstrate 8.0 and 9.0% increases in mortality risk due to CO exposure, respectively. Respiratory-related risks due to CO are higher in the MT weather type (RR ¼ 1.212 (95% CI 1.091e1.345)). This estimate is significantly greater than the DM and MP weather types (Table 4). NO2 exposure in the springtime also poses high risk of respiratory-related mortality, which is greatest in the DT and MT weather types (i.e., RR ¼ 1.147 (95% CI 1.027e1.280)) and RR ¼ 1.148 (95% CI ¼ 1.048e 1.256), respectively, yet these are not significantly greater than the remaining four weather types. However, as shown in Table 4, the risk estimates within the DT and MT weather types due to SO2 and

Table 4 Relative risk of mortalitya (RR) due to air pollution within synoptic weather type pooled across ten cities for spring and summer seasonsb (1981e1999). Analysis completed at population-weighted means, with lower and upper 95% confidence intervals (CI) displayed. Statistically significant differences between the weather types were assessed with respect to season and mortality type (CVD or Respiratory) for each pollutant. CO

Spring Cardiovascular

Respiratory

Summer Cardiovascular

Respiratory

NO2

RR

95% CI

DM DP DT MM MP MT TR DM DP DT MM MP MT TR

1.061 1.064 1.066 1.049 1.052 1.071 1.031y 1.079y 1.115 1.107 1.188 1.088y 1.212 1.086y

(1.038, (1.036, (1.017, (1.022, (1.024, (1.018, (1.009, (1.032, (1.061, (0.984, (1.095, (1.015, (1.091, (1.037,

DM DP DT MM MP MT TR DM DP DT MM MP MT TR

1.043 1.060 1.079 1.053 1.079 1.036y 1.060y 1.087y 1.123 e 1.090y 1.107 1.263 1.043

(1.024, (1.029, (0.950, (1.021, (1.039, (0.996, (1.021, (1.040, (1.038, (e, e) (1.020, (1.107, (1.067, (0.921,

SO2

RR

95% CI

1.084) 1.093) 1.118) 1.077) 1.08) 1.126) 1.053) 1.127) 1.172) 1.245) 1.290) 1.165) 1.345) 1.136)

1.065 1.039y 1.081 1.033y 1.060 1.034y 1.034y 1.093 1.079 1.147 1.120 1.120 1.148 1.070y

(1.042, (1.019, (1.038, (1.007, (1.035, (0.995, (1.011, (1.050, (1.034, (1.027, (1.062, (1.064, (1.048, (1.021,

1.062) 1.091) 1.225) 1.085) 1.122) 1.077) 1.100) 1.135) 1.215)

1.047 1.041 1.037 1.040 1.049 1.053 1.073y 1.080 1.123 e 1.091 1.151 1.069y 1.146y

(1.030, (1.017, (0.880, (1.016, (1.021, (1.022, (1.031, (1.040, (1.058, (e, e) (1.040, (1.066, (1.003, (1.059,

1.164) 1.107) 1.495) 1.181)

O3

RR

95% CI

1.087) 1.059) 1.126) 1.06) 1.086) 1.074) 1.057) 1.137) 1.126) 1.280) 1.181) 1.179) 1.256) 1.121)

1.021y 1.021y 1.061 1.018y 1.008y 1.025y 1.023y 1.023y 1.034y 1.110 1.077 1.026y 1.138 1.074y

(1.005, (1.004, (1.018, (1.006, (1.001, (0.995, (1.009, (1.004, (1.009, (0.996, (1.030, (1.007, (1.054, (1.039,

1.064) 1.065) 1.222) 1.063) 1.079) 1.085) 1.116) 1.121) 1.193)

1.034y 1.028y 1.115 1.029y 1.024y 1.028y 1.040y 1.069 1.089 e 1.031y 1.129 1.012y 1.079

(1.022, (1.008, (0.917, (1.012, (1.005, (1.006, (1.014, (1.033, (1.089, (e, e) (1.007, (1.068, (1.012, (1.018,

1.146) 1.242) 1.139) 1.241)

RR

95% CI

1.037) 1.037) 1.105) 1.03) 1.015) 1.055) 1.036) 1.043) 1.059) 1.238) 1.127) 1.045) 1.228) 1.109)

1.020y 1.021y 1.061 1.020y 1.039 1.036 1.051y 1.053y^* 1.083 1.124^ 1.067y* 1.088 1.143* 1.114y

(1.013, (1.004, (1.019, (1.001, (1.020, (1.001, (1.008, (1.017, (1.040, (1.001, (1.017, (1.044, (1.054, (1.053,

1.038) 1.039) 1.105) 1.038) 1.058) 1.072) 1.096) 1.091) 1.127) 1.262) 1.120) 1.134) 1.240) 1.177)

1.045) 1.048) 1.357) 1.047) 1.044) 1.051) 1.067) 1.106) 1.089)

1.048 1.081 1.089 1.041y 1.043y 1.059 1.054y 1.070 1.059 e 1.096 1.106 1.085 1.165y

(1.033, (1.051, (0.892, (1.019, (1.009, (1.031, (1.022, (1.039, (1.013, (e, e) (1.052, (1.024, (1.020, (1.081,

1.064) 1.112) 1.328) 1.064) 1.077) 1.087) 1.086) 1.103) 1.107)

1.056) 1.194) 1.012) 1.145)

1.143) 1.194) 1.155) 1.256)

y Denotes values that are significantly less than bolded estimates when compared within the same season and mortality category. When more than one pair of significant differences occur (e.g., Respiratory-related mortality due to O3 in spring), corresponding pairs contain same symbol (^or *), with bolder values being significantly greater. a Statistically significant RR found when 95% lower CI > 1.0 (p < 0.001). b Only spring and summer seasons presented as fall and winter did not show significant differences by weather type. Data presented in Figs. 3e6.

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O3 are significantly greater than other weather types, highlighting the influences of weather on risk. Further, in the tropical weather types (DT and MT) in spring, there is a >10% increase in respiratory-related mortality risk for all pollutants (Table 3). This trend extends across the MT weather type for all pollutants in springtime, with a 16.0% increase in mortality risk from baseline mortality. The MT respiratory-related estimates are also significantly higher than the remaining two mortality categories for any air pollutant. Summertime respiratory-related RR estimates closely reflect that of springtime; however, the model was not able to predict respiratory RR on DT days in the summer. This is due to a low number of DT days (average frequency of 2.1% for the summer season), along with very few respiratory disease-related deaths occurring on these days. CO and NO2 demonstrate the highest associations with respiratory mortality in summer, with CO risks highest in the MT weather type (1.263 (95% CI 1.067e1.495)). This estimate is significantly higher than DM and MM. Alternatively, NO2 risks are the highest in cold DP and MP weather types (1.123 (95% 1.058e1.193)) and 1.151 (95% CI 1.066e1.242)), respectively. Winter season respiratory mortality risk estimates are lower than that of spring and summer, and comparable to the fall. There is no significant difference between air pollution types, and although some differences can be found between weather types, these are rarely significant. The dry-weather subsets (DM and DP) are of most concern, with each exhibiting an average increase of 7.8% in respiratory mortality due to any air pollutant exposure. The DM

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weather type contains the highest average RR, and a 10.2% increase in risk due to O3 exposure. In the fall, risk estimates tend to be lower than in the remaining three seasons; however, in many cases they are still statistical significant, with individual air pollutants associated with an average increase of 5e10% in respiratory related mortality (Table 3). 3.4. Cardiovascular-related mortality Overall, the risk of cardiovascular-related mortality due to air pollution increased significantly from baseline for 86% of the cases modelled. In the majority of cases, the increase in RR is 5% or less due to the individual air pollutants, and none greater than 10% (Table 3). The greatest differences between cardiovascular and respiratory risk estimates are found in the spring season within the MT weather type, where respiratory risk estimates are 14% above that of cardiovascular risk for CO, and 11% above for the three remaining pollutants. When hot and dry air is present (DT), results consistently demonstrate average increases of 5e10% in cardiovascular-related RR for all pollutants in the spring and summer seasons. Separating the RR estimates for each air pollutant on the DT days in the springtime, cardiovascular risk due to SO2, NO2, and O3 is 3.5, 4.4, and 3.3% greater, respectively, than the average of the remaining weather types (Table 4). The greatest number of significantly different RR estimates was found when comparing the DT weather type to the others. For example, during summertime DT weather, cardiovascular-related mortality due to

Fig. 3. Pooled relative risks of cardiovascular e (CVD) and respiratory-related mortality (at PWM, with 95% CI), due to CO within six weather types (10 Canadian cities: 1981e1999). Asterisk (*) indicates the RR estimate of respiratory (Resp) mortality to be significantly greater than mortality from CVD for the given weather type and season.

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SO2 was significantly greater than the RR estimates in the remaining weather types. The remaining pollutants (CO, NO2 and O3), demonstrate less difference by weather type, yet have a higher average risk of cardiovascular-related mortality throughout the year, at 5.8, 4.5, and 6.0% average increases, respectively. Fall, winter, and springtime exposures to SO2 result in the lowest estimates of cardiovascular-related RR. The highest cardiovascular-related risk estimates occur in the spring, specifically within DT air, and are weakest and least variable in the fall and winter (Figs. 3e6). 4. Discussion In the current study, we analyse the association between shortterm exposure to individual air pollutants and mortality within six different weather types and transitional weather, and stratify by season and the cause of mortality. Across Canada on a yearly basis, we find the greatest difference in RR within the hottest weather types of DT and MT (þ11.0 and þ9.0%). Studies relating high temperature days to respiratory and cardiovascular mortality (D’Ippoliti et al., 2010; Hoffmann et al., 2008) report a more prominent effect of heat waves on respiratory causes, with the study in Europe by D’Ippoliti et al. (2010) suggesting this is due to increased susceptibility among people with

pre-existing chronic respiratory diseases. However, our findings suggest that the interaction between temperature and specific air pollutants during these extreme heat days work synergistically to negatively affect respiratory mortality, and to a smaller extent cardiovascular mortality. Anderson and Bell (2009) also found respiratory mortality effects to be greater in both cold and heat. Alternatively, some studies have found cardiovascular ailments to be more sensitive to certain air pollutants than weather, namely PM. For example, Stafoggia et al. (2008) found no interactions between two environmental exposures (PM10 and temperature) with respiratory mortality, yet there was a significant linear interaction with cardiovascular mortality. The mortality effects of PM10 were also significantly higher on warmer days. Further, chronic exposure, rather than acute, has been shown to be strongly associated with cardiovascular mortality and air pollutants, with PM exposure of most concern (Dockery et al., 1993; Pope et al., 2004). The significant differences that are found between weather types, specifically in the spring and summer seasons (Table 4, Figs. 3e6), reveal weather-pollution interactions. Pollution chemical interactions and reaction rates vary with temperature, sunlight, and moisture conditions, resulting in higher pollution levels. This is most evident with O3 in the DT weather type in this study and others (Davis and Kalkstein, 1990; Davis et al., 2010), with highest concentrations during the summer season (Fig. 1). High levels of NOx in urbanized areas (due to combustion emissions from

Fig. 4. Pooled relative risks of cardiovascular e (CVD) and respiratory-related mortality (at PWM, with 95% CI), due to NO2 within six weather types (10 Canadian cities: 1981e 1999). Asterisk (*) indicates the RR estimate of respiratory (Resp) mortality to be significantly greater than mortality from CVD for the given weather type and season.

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Fig. 5. Pooled relative risks of cardiovascular e (CVD) and respiratory-related mortality (at PWM, with 95% CI), due to SO2 within six weather types (10 Canadian cities: 1981e1999). Asterisk (*) indicates the RR estimate of respiratory (Resp) mortality to be significantly greater than mortality from CVD for the given weather type and season.

vehicles, industry, and natural processes), lead to even greater O3 production via the photolytic cycle (Finlayson-Pitts and Pitts, 2000; Notario et al., 2012). Ozone has been continuously highlighted in the literature as a harmful ground level pollutant and is projected to increase in the 21st century (Bell et al., 2007; Holloway et al., 2008; Knowlton et al., 2004). Synoptic weather typing accounts for the variables responsible for this ozone formation (clear, dry, high pressure, low winds), and thus we demonstrate its use as a holistic way to account for and understand how the entire ambient situation acts as a modifying factor on the air pollutionemortality relationship. Correlations between air pollutants, as well as between pollution and air temperature, often result in conflicting evidence for an effect modification on human health outcomes; hence it becomes more difficult to separate the model signals for individual variables. For example, Samoli et al. (2007) addressed confounding by air pollutants, where adjusting CO estimates by NO2 resulted in lowering of the single CO estimate, yet remained marginally statistically significant. Smoyer et al. (2000a) found the MT weather type to have a greater negative health impact on mortality than high concentrations of total suspended (TSP) particles or O3 in Birmingham, USA. Similarly, extreme heat days in Australia (based on the ‘temporal synoptic index’), significantly increased mortality risk, with O3 confounding the results on humid, hot days, and PM doing so on dry, hot days (Vaneckova et al., 2008). Alternatively

Keatinge and Donaldson (2001) found that excess deaths were associated with cold weather patterns more so than ambient SO2 and CO concentrations in the Greater London area (1976e1995). Many studies report that the temperature effect on mortality is greater when pollutant levels are higher, commonly referring to O3, and emphasize the synergism between air pollution and heat or cold. For example, Ren et al. (2007) found a positive modifying effect of O3, with higher levels resulting in stronger temperaturerelated cardiovascular mortality across different regions of the United States in the summer. Cheng et al. (2009) also found elevated mortality associated with heat and air pollution in five major Canadian cities, with 80% attributable to air pollution, and 20% to temperature. It is suggested that such temperature effects are likely to persist even after controlling for multiple air pollutants in modelling, particularly O3 and PM2.5 (O’Neill et al., 2005). Cheng et al. (2009) found that three pollutants (O3, SO2, and NO2) were associated with w75% of total mortality due to air pollution, with O3 the most significant, accounting for 33% of the total. An understanding of the air pollution effects on cardiovascular events has proven much more difficult to achieve than for respiratory disease (Brunekreef and Holgate, 2002). Further, specific cardiovascular diseases have been closely associated with heatrelated mortality, such as ischemic heart disease, congestive heart failure, and myocardial infarction (MI) (Basu, 2009). Hanna et al. (2011) found MI to be affected by O3 exposure only under MTþ

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Fig. 6. Pooled relative risks of cardiovascular e (CVD) and respiratory-related mortality (at PWM, with 95% CI), due to O3 within six weather types (10 Canadian cities: 1981e1999). Asterisk (*) indicates the RR estimate of respiratory (Resp) mortality to be significantly greater than mortality from CVD for the given weather type and season.

(an extreme subset of MT) conditions, and asthma affected by O3 under both DT and MTþ. Hence, extreme weather conditions plus air pollution can elicit a greater overall effect of the full atmospheric environment. Higher winter mortality rates (as compared to summer) in Canada were also found by Rainham and Smoyer-Tomic (2003) and Martin et al. (2012). The latter researchers emphasized the finding that cold-related mortality (versus heat-related) was a greater issue across Canada (excess mortality per 100,000: 58.5 versus 1.4). Exposures to both hot and cold temperatures give rise to direct cardiovascular stress (Keatinge and Donaldson, 1995; Huynen and Martens, 2001; McGeehin and Mirabelli, 2001). Hence, the relationships found between cardiovascular-related mortality and air pollution may be confounded to a greater extent by weather, and aids in explaining why understanding the air pollution effects on cardiovascular disease are more difficult to achieve (Brunekreef and Holgate, 2002). Carbon monoxide has significant associations with mortality in all seasons, agreeing with other research that found significant associations with all-cause mortality (Burnett et al., 1998b) and total and cardiovascular mortality (Samoli et al., 2007). Burnett et al. (1998b) found the mortality risk attributed to CO to be greater than that of any pollutant examined, and thus CO in particular should be considered a potential cause of increased

mortality in urban populations. These researchers, and the current study, have shown that CO, as well as NO2, are also equally important components of the urban pollution mixture as PM and O3, if not more. The presence of aeroallergens in the spring is a possible explanation for the heightened effects of air pollution across weather types; however, these were not accounted for in the current study. Aeroallergens have been associated with significant increased risk of asthma exacerbations, hospitalizations, allergic disease, and morbidity (Reid and Gamble, 2009; Cakmak et al., 2011; Dales et al., 2004). Further heightened risk in spring may also be due to large fluctuations in weather, as well as negative impacts of early season high temperatures (Sheridan and Kalkstein, 2010). Cardiovascularrelated RR in tropical weather was significantly greater in spring than in the summer, highlighting how time of season is a significant time variable, as also found in earlier synoptic studies (Kalkstein and Davis, 1989; Kalkstein and Smoyer, 1993; Smoyer et al., 2000a,b). The significant and elevated respiratory-related RR estimates on moderate or cool days indicates that all air pollutant exposure is cause for concern. Similar results were found by Cheng et al. (2009), where the ‘other’ groups (associated with good air quality and comfortable conditions) were found to have elevated mortality due to air pollution, with significance in four of five

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tested cities for specific pollutants. Such pollutant specific results, including here and by others (Basrur et al., 2001; Lambert et al., 1998; Vedal et al., 2002; Rainham et al., 2005), suggest that evidence to propose clear pollution concentration thresholds based on mortality does not exist. Any reduction in ambient pollution levels is a benefit to human health, and no threshold has been defined below which no effects to human health are likely (Brunekreef and Holgate, 2002). Although the pooled estimates of all ten cities compare well with similar time-series pollution and temperature studies (e.g., Anderson et al., 2004; Baccini et al., 2008; Stieb et al., 2003; Thurston and Ito, 2001), the combined values cannot be applied to each city, as they are overall values for the entire country/area. City-specific information is provided in the Supplementary Tables; however, specific examination of these results should be conducted so that policies can be implemented that are city-specific. Further, city characteristics, such as topography, weather patterns, and anthropogenic pollution sources, can be considered. This can better advise local health care institutions, governments, and weather services to set targeted guidelines for air-pollution-health relationships. 5. Conclusions Air pollution has a significant influence on acute human mortality, which is overall greater than influences from weather, and varies with season and the entire weather situation present. Stronger effect estimates are found for respiratory mortality than cardiovascular or all-cause. The risk of dying due to exposure to all air pollutants from respiratory-related causes is significantly higher than that of cardiovascular causes in 61% of the cases, with the RR found to be 6e10% greater, on average. The combined effect of weather and air pollution is greatest when tropical-type weather is present in the spring or summer. Dry tropical days are found to be most harmful in spring for both causes of mortality, and in the summer for cardiovascularrelated mortality. The spring season presents the overall greatest risk of respiratory-related death compared to cardiovascular; in particular, the risks due to air pollution exposure are highest on DT and MT days, with CO and NO2 the most harmful air pollutants. These results underscore the importance of addressing the cause of mortality, full weather type, and season, when estimating the mortality risk of air pollution exposure. This epidemiological study gives evidence to environmental-health policy makers to proactively implement prevention strategies to reduce all levels ambient air pollutants. Further, since all weather and air pollution types present significant health risks, it is suggested that any reduction in ambient pollution levels is a benefit to human health. The investment in early warning systems based on synoptic meteorological forecasts, such as an integrated weather-pollution index, is suggested in order to predict accurate spatiotemporal health outcomes for the public. This study demonstrates the potential usefulness in estimating precise short-term future health/ pollution problems associated with specific incoming synoptic weather patterns for each season. Acknowledgements The authors would like to thank Environment Canada for providing the air pollution data and the reviewers for their helpful comments. Funding for completion of this project was generously provided to Dr. Jennifer Vanos by a Natural Sciences and Engineering Research Council (NSERC) Postdoctoral Fellowship from the Government of Canada.

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Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.envpol.2013.11.007.

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