1. Background
Air pollution is a major social problem, particularly in developing countries, where the rapid expansion of industries, cities, and traffic is the main cause of increased air pollution (1, 2). In general, air pollution is a mixture of several components, including particulate matter (PM), gases, organic compounds, and metals (3, 4). Epidemiological and clinical evidences show that short- and long-term exposure to outdoor air pollution causes adverse health effects and leads to an increase in mortality and various diseases, particularly cardiovascular diseases and stroke (3, 5-11). The World Health Organization (WHO) defines stroke as “rapidly developed clinical signs of focal (or global) disturbance of cerebral function, lasting more than 24 hours or leading to death, with no apparent cause other than of vascular origin” (12). Stroke is the second most common cause of death and the sixth most common cause of disability worldwide (13-16). Recent studies in North America, Europe, and Asia provide evidence of the effects of environmental pollutants on stroke mortality and hospitalization. Among air pollutants, PMs are the largest cause of mortality and various diseases such as stroke. Stroke mortality contributes about 10% of all mortalities worldwide. The World Health Organization estimates that there were 15.3 million strokes worldwide in 2002, of which more than a third (5.5 million) resulted in death (3, 17, 18).
Although several studies are being conducted to prevent strokes in high-income countries, more than 85% of the strokes occur in low-income and middle-income countries (17). Epidemiological studies indicate a correlation between long-term exposures with air pollution, in particular PM2.5, and malfunction of the central nervous system (CNS), premature deaths, heart disease, stroke, respiratory diseases, and lung cancer (19-26). Studies by Global Burden of Disease (GBD) showed that in 2010, about 2.3 million deaths (5% of total global deaths) occurred worldwide due to air pollution by PM2.5. This pollutant is ranked as the sixth biggest risk factor for premature death (19, 27). Recent studies show that in areas with high levels of PM2.5, increased incidence of cardiovascular mortality and stroke is reported (6, 28-30). Lee et al. in 2014 conducted a study in Korea on the relation between exposure to air pollution and cardiovascular disease. Their results showed that PM2.5 and PM10 had adverse health effects, such as heart disease, stroke, and high blood pressure; these health effects have a stronger correlation with PM2.5 (31). Research studies conducted in 25 communities in America show that a 10 µg/m3 increase in PM2.5 raises all causes of mortalities by 1.2% and stroke mortalities by 1.36% (32). In 2015 Shah et al. conducted a study on the relationship between short-term exposure to air pollution and stroke. Their findings showed that the increase in the relative risk of hospital admissions for stroke or stroke mortality was 1.011 for a 10 μg/m3 increase in PM2.5 concentration (33). At present, many cities around the world face environmental problems related to air pollution (34-38). The impact of environmental factors on stroke mortality is less commonly considered. Although some studies show that air pollution is a modifiable and important risk factor (33).
2. Objectives
Due to the high air pollution in Mashhad (Iran), caused mainly by the transport and the industry sectors, this study aims to analyze the relationship between the amount of PM2.5 and stroke mortality rate in Mashhad during 2014 and 2015.
3. Methods
3.1. Sample Size
In this study, all recorded data on 24-hour average concentration of PM2.5 pollutant (730 data points) all patients who died of a stroke in Mashhad (475 deaths) and all data pertaining to meteorological parameters (wind speed, relative humidity, temperature, and rainfall) in 2014 and 2015 have been used.
3.2. Data Collection
The data related to the 24-hour average concentration of PM2.5 pollutants were collected from the monitoring center for environmental pollutants in Mashhad in the format of excel files. In this center, the concentration of air pollutants is automatically measured through the air quality index (AQI) at 10 stations using the Environment S.A France device. Data of patients who died due to stroke in 2014 and 2015 in Mashhad were collected by referring hospitals, reviewing the dossiers of patients, and using questionnaires. Additionally, data on meteorological parameters were collected from the Bureau of Meteorology in Khorasan Razavi Province in the format of an excel file.
3.3. Statistical Analysis
For statistical analysis, SPSS software was used. To investigate the normal distribution of the variables, the Kolmogorov–Smirnov test was applied. Pearson correlation and linear regression were used to determine the relationship between variables and predict the dependent variable. Tables and figures were drawn through excel and GraphPad Prism software.
4. Results
The results presented in Table 1 show that the Pearson correlation coefficient between the monthly average concentration of PM2.5 pollutant and the monthly average wind speed in 2015 is -0.624 (P = 0.03). However, there is no significant correlation between PM2.5 concentration and other meteorological parameters (relative humidity, temperature, and rainfall).
Table 1.The Pearson Correlation Between the PM2.5 Pollutant and Meteorological Parameters (Wind Speed, Relative Humidity, Temperature and Rainfall)
| Year | Pearson Correlation Test | Wind Speed (km/h) | Temperature (ºC) | Rain Fall (mm/h) | Relative Humidity (%) |
|---|---|---|---|---|---|
| 2014 | Sig (2-tailed) | 0.381 | 0.696 | 0.217 | 0.654 |
| Pearson correlation | -0.278 | 0.126 | -0.385 | -0.145 | |
| 2015 | Sig (2-tailed) | 0.03 | 0.094 | 0.266 | 0.141 |
| Pearson correlation | -0.624a | -0.505 | 0.349 | 0.451 |
aCorrelation is significant at the 0.05 level (2-tailed)
As shown in Table 2, the investigation of the average concentration of PM2.5 pollutants and stroke mortality rate in different seasons of 2014 and 2015 shows that the highest concentration of PM2.5 pollutant and the highest rate of stroke mortality are observed in the autumn season. Table 3 shows the results of the Pearson’s correlation between the PM2.5 pollutant with the stroke mortality in two age groups (30 to 59 years and 60 years and older), gender, and month and season of the year in 2014 and 2015. The results show that the Pearson correlation coefficients between this pollutant and stroke mortality in different months and seasons in 2014 are 0.610 (P = 0.035) and 0.997 (P = 0.003), respectively. Moreover, the Pearson correlation between the PM2.5 pollutant and stroke mortality in the age group of 30 - 59 years in different months in 2014 and 2015 are 0.765 (P = 0.004) and 0.628 (P = 0.029), respectively.
Table 2.The Average Concentration of PM2.5 Pollutant and Mortality Rate in Different Seasons in 2014 and 2015
| Year | Variable | Season | |||
|---|---|---|---|---|---|
| Spring | Summer | Autumn | Winter | ||
| 2014 | Average concentration of PM2.5 | 25.67 ± 17.16 | 30 ± 20.58 | 34.5 ± 23 | 25.82 ± 13.06 |
| Number of stroke mortality | 58 | 63 | 68 | 59 | |
| 2015 | Average concentration of PM2.5 | 24.08 ± 16.08 | 23.70 ± 14.85 | 26.71 ± 16.71 | 26.71 ± 15.68 |
| Number of stroke mortality | 55 | 45 | 66 | 61 | |
Table 3.Pearson’s Correlation Between the PM2.5 Pollutant and the Stroke Mortality in Different Age Groups, Genders, Months and Seasons in 2014 and 2015
| Variable | Pearson Correlation Test | Stroke Mortality | Age | Gender | ||
|---|---|---|---|---|---|---|
| 30 - 59 | ≥ 60 | Female | Male | |||
| 2014 | ||||||
| Different months | Sig (2-tailed) | 0.035 | 0.004 | 0.365 | 0.087 | 0.076 |
| Pearson Correlation | 0.610a | 0.765b | 0.288 | 0.515 | 0.531 | |
| Different seasons | Sig (2-tailed) | 0.003 | 0.058 | 0.520 | 0.494 | 0.171 |
| Pearson Correlation | 0.997b | 0.942 | 0.480 | 0.506 | 0.829 | |
| 2015 | ||||||
| Different months | Sig (2-tailed) | 0.271 | 0.029 | 0.531 | 0.298 | 0.644 |
| Pearson Correlation | 0.346 | 0.628a | 0.201 | 0.328 | 0.149 | |
| Different seasons | Sig (2-tailed) | 0.098 | 0.017 | 0.211 | 0.244 | 0.403 |
| Pearson Correlation | 0.902 | 0.983a | 0.789 | 0.756 | 0.597 | |
aCorrelation is significant at the 0.05 level (2-tailed).
bCorrelation is significant at the 0.01 level (2-tailed).
The correlation in different months of 2014 is stronger. Figure 1A and Figure 1B show the relation between PM2.5 pollutant and stroke mortality in different months of 2014 and 2015. As seen in Figure 1A, in 2014, the highest concentration of PM2.5 pollutant (42.939 ± 16.02 μg/m3) and the highest rate of stroke mortality (30 deaths) were observed in October. Figure 1B depicts that in 2015, the highest concentrations of PM2.5 pollutant (28.934 ± 6.42 μg/m3 and 28.741 ± 9.76 μg/m3, respectively) were detected in September and December. The highest rate of stroke mortality (29 deaths) was seen in December. The results of linear regression between PM2.5 pollutant and stroke mortality in different seasons of 2014 show that the correlation between these two variables is R = 0.997.
Figure 1.
(A) The trend of changes in concentration of PM2.5 and trend of changes in stroke mortality in different months in 2014 and (B) the trend of changes in concentration of PM2.5 and trend of changes in stroke mortality in different months in 2015.
5. Discussion
The results of this study indicate a significant negative correlation between PM2.5 and wind speed only in different seasons of 2015. However, there was no significant relationship between PM2.5 concentration and other meteorological parameters. Also, Huang et al., in 2015, in Beijing and Rasheed et al., in 2015, in Pakistan observed a significant negative relationship between wind speed and PM2.5 (39, 40). Lin et al., in an investigation of the relationship between PM2.5 and meteorological parameters, observed that there is a negative correlation between the concentration of PM2.5 and wind speed. Also, they observed a positive relationship between this pollutant and temperature parameter in spring and summer, but not in autumn and winter (41). While the study of Bhaskar et al. in India showed that there is a significant negative relationship between suspended particulate matter concentration and rainfall (42). Zhang et al. also investigated the relationship between different air pollutants (PM2.5, PM10, CO, SO2, NO2, and O3) and meteorological parameters in three megacities in China. Their results were similar to our study about the relationship between wind speed and PM2.5 (43).
The findings also show that in 2014 and 2015, the daily average concentration of PM2.5 pollutant (210 and 165 days, respectively) was higher than the standard value suggested by WHO (> 25 μg/m3). In 2015, there was an increase in the wind speed and a reduction in the number of days in which the average concentration of PM2.5 was more than the WHO standard. This could reveal the role of wind in the distribution of PM2.5. The results of our study show that the highest PM2.5 concentration in 2014 and 2015was observed in autumn. Li et al. also studied the concentration of PM2.5 at 11 stations in one of the Chinese cities. In addition, their results indicated that the average concentration of this pollutant was higher in autumn and winter (41).
According to the results, the annual average concentration of PM2.5 decreased from 29.261 μg/m3 in 2014 to 25.283 μg/m3 in 2015. Also, the annual rate of stroke mortality decreased by 4.4% in 2015. There is a significant positive relationship between PM2.5 pollutant and stroke mortality, especially in the age group of 30 - 59 years. In a study, Hadei et al. studied stroke deaths due to PM2.5 exposure in ten cities of Iran. Their findings showed that Tehran, Mashhad, and Isfahan had the highest mortality rate due to stroke. The average mortality due to PM exposure in all the cities surveyed was 41 per 100000 people. The results of this study indicated the necessity of urgent actions to improve outdoor air quality in Iranian cities (44). Lin et al. investigated the relationship between ambient PM2.5 and stroke in six low- and middle-income countries. In this study, 45625 people were studied, and information about ambient PM2.5 levels was estimated using satellite data. The results of their study showed that the odds of stroke were 1.13 for each 10 μg/m3 increase in PM2.5 (45).
Pascal et al. in 2014 conducted a study on the short-term effects of particulate matter (PM2.5, PM10e2.5, and PM10) on mortality in nine French towns. The results show a direct correlation between mortality and the concentration of PMs in the air (46). Chalbot et al., in 2014, investigated the correlation between the non-randomized process of cardiovascular disease mortality, stroke, and lung cancer and the reduction in PM2.5 pollutant in Arkansas. Their results showed that the annual average concentration of PM2.5 reduced from 14.5 μg/m3 in 2000 to 11.5 μg/m3 in 2010, causing a decrease in cardiovascular mortality, stroke, and lung cancer by 21%, 30%, and 2% respectively (47).
5.1. Conclusions
The results indicate that there is a significant positive relationship between the level of PM2.5 pollutant and stroke mortality rate in Mashhad, especially in the age group of 30 - 59 years. Overall, the findings of this study show that improvements in air quality and reduction in concentrations of PM2.5 pollutant in Mashhad can lead to a reduction in the rate of stroke mortality in this city.
