| BackgroundAir pollution is a prominent environmental problem in our China, especially in large cities such as Beijing, Shanghai and Guangzhou. The state environmental protection department monitoring data showed that only 4.1% of monitoring city meet the air quality standard of China. The annual concentration of PM2.5 (fine particularte matter) in 10 poorest air quality cities is more than 3 times of the air quality standard of China. PM2.5 has become the main "contributor" of urban air pollution in China. PM2.5 is a major contributor to the air pollution in urban areas in China. It has been demonstrated that PM2.5 could increase the population health risk, which leads to lose of public health and economy.Guangzhou is the provincial city of Guangdong, and the third largest city in China. It’s also the economic and cultural center in south China. Due to the extensive economic growth mode, the air pollution gained increasingly attentration from the public. Air pollution become one of the hot topic in the masses of people.Although air pollution situation in Guangzhou has been improved, it is far from the air quality standard of the World Health Organization (WHO). The air pollution prevention and control is a long and arduous task.People exposed to atmospheric pollution by various ways. Commuting is one of high exposure modes of air pollutants in daily life, especially in traffic busy cities. Commuting refers to the residents in back and forth between the residence and work place, which is an important part of modern people activity pattern.The WHO report showed that people used about 1 hour to 1.5 hours to commuting every day. And the exposure dose of PM2.5 during commuting accounted for about 15% of the total environment exposure. Individual PM2.5 exposure dose increase 2.5μg per 1 hour traffic time. The Guangzhou commuter environment has high concentration of PM2.5 exposure. Morever, according to the China’s labor market development report in 2014, Guangzhou was one of the cities with longest commuting time, which was close to 90 minutes per day. Therefore, it’s significant to strengthen the research of PM2.5 exposure in different commuting, and it’s the base for accurate assessment of PM2.5 exposure among guangzhou citizens.Objective1. To find the difference of PM2.5 exposure concentration in four commuting modes (walking, bicycle, bus and subway), and the influencing factors in commuting exposure to PM2.5. In order to guide the public to choose appropriate commuting mode.2. By discussing the influence of environmental and meteorological factors on commuter exposure to PM2.5, to improve the accuracy of PM2.5 exposure assessment, and provide basic data for health risk assessment.Content1. In this study we monitored PM2.5 exposure concentration by walking, bicycle, bus and subway commuter environment. Comparing the difference between walking, bicycle, bus and subway commuter exposure to PM2.5. At the same time, we collect the fixed stations exposure concentrations of PM2.5 at the same time point comparing with all modes exposure.2. To explore the influence factors of commuter exposure to PM2.5, analysis different time periods, time factors such as week, season, and the effect of meteorological factors on the PM2.5 exposure concentration between different modes of commuting.3. Combining Guangzhou citizens’ respiratory rate in walking, bicycle, bus and subway commutting mode to calculate the exposure dose of different commutting modes. Analysis the influence of commutting mode for PM2.5 exposure dose. And we also analyse the influence of different time periods, the week days, season on single exposure level.4. Comparing to walking, bicycle monitoring in the process of presence of greening in roadside effect on commuter exposure.Difference between bus, subway monitoring in the process of walking to the platform stage, waiting on the platform and the in-vehicle exposure.MethodThe PM2.5 exposure assessment was carried out from January to December 2015 in Guangzhou. The commuting modes were walking, bicycle, bus and subway.The commutting exposure measurements were carried out in a fixed line route with 4 km length accross yuexiu and liwan district. Our measurement was taken on Monday, Wednesday, Friday and Sunday in the second week of each month. Samples were collected at three time periods in each monitoring day:morning (7:00-9:00), afternoon (11:00-13:00) and evening (17:00-19:00). Four monitors responsible for commuting mode to measure commuter exposure at the same time in the established route in each period. A roundtrip commuting exposure measurement of four modes in each period of every monitoring day. Took the average of PM2.5 commuting exposure concentration per minute in one way route monitoring data as a sample of commuter exposure to PM2.5. Respiratory rate of each commuting mode was combined to calculate the average commutting exposure dose of each mode in one-way.Descriptive analysis, average, standard deviation,25,50,75 percentile, the maximum and the minimum were used to describe air environmental quality during the experimental monitoring including AQI (Air Quality Index) and PM2.5. And the meteorological condition include temperature, relative humidity, precipitation per hour average data. We use mean±SD to describe different months, seasons, time of the day in four commuter exposure concentration to PM2.5 and exposure dose.Using one-way ANOVA to compare the difference between months, seasons, the day of times on four commutting exposure. The comparison of two sample means by t test. Using correlation analysis and investigate the relationship between meteorological factors, fixed monitoring PM2.5 exposure concentration, AQI index and commuter exposure to PM2.5. Multiple linear regression was used, to find out the influence factors of commuter exposure PM2.5 and the influence degree of these factors.Results1. This research collected 1123 valid samples, including 284 walking samples, 281 bicycle samples,278 bus samples, and 280 subway samples. During the study period AQI index ranged from 24 to 100, the temperature ranged from 6 to35℃, humidity ranged from 25% to 97%, the rainfall ranged from 0 to 10 mm, the wind speed was priority to secondary degree wind, dominant wind direction as the north wind and the north east.2. One-way’s average exposure time for Walking, bicycle, bus and subway were 65.6,28.2,33.0, and 16.0 min, respectively. The average exposure concentrations were 46.1,46.0,29.3,27.7μg/m3, respectively. The average exposure concentration in fixed site of environmental monitoring was 44.4μg/m3.3. The PM2.5 concentrations presented analogous U monthly variation characteristics for four commuter mode. The exposure peak was appeared in January and October; the lowest exposure concentration was during June and July. From the point of changing seasons, exposure concentration was minimum in summer, followed by spring and autumn, the highest exposure concentration was in winter. The walking exposure concentration in spring, summer, autumn and winter was 28.3,46.3,65.4 and 44.4μg/m3, respectively; bicycle exposure concentration was 44.3,28.0,46.0 and 28.0μg/m3, respectively; bus was 17.4,25.7,45.2 and 30.6 μg/m3, respectively; the subway was 25.9,18.0,27.4,18.0μg/m3, respectively; environmental exposure concentration in each season was 32.7,47.0,59.3,39.3μg/m3.4. Commuter and environmental exposure concentration on Wednesday and Sunday were higher than Monday and Friday (P< 0.05). Working day is slightly lower than the average exposure concentration of weekend, but there was no statistically significant difference (P> 0.05).5. The morning, afternoon and evening walking exposure concentrations of PM25 were 49.1,48.4,49.1μg/m3, respectively; bicycle were 51.6,47.3,39.1μg/m3, respectively; bus were 33.5,28.9,25.5μg/m3, respectively; the subway were 30.7,26.0, 26.4μg/m3, respectively; but there were no statistical significance of the exposure between different periods of a day (P> 0.05). Environmental exposure concentration from fixed site were 48.7,44.7,40.0μg/m3 in morring, afternoon, evening respectively, and there were statistically significant differences between them (P< 0.001).6. The one-way exposure dose to PM2.5 is the highest in bicycle commuting, which was 27.2±24.5μg; followed by walking, which was 23.8±18.1μg. Bus exposure dose was 6.1 ± 5.0μg. The subway exposure dose was the lowest,3.1± 2.0μg. One-way exposure dose in each month by walking was ranged from 12.5 to 12.5μg, bicycle was 12.8～62.9μg, bus was 3.1～13.2μg, and the subway was 1.7～6.1 μg. Environmental exposure dose of four commuter have similar characteristic with exposure concentration, winter> spring and autumn> summer. On Wednesday and Sunday, four commuter exposure levels are higher than that on Monday and Friday. Walking, bicycle exposure dose in morning and afternoon were higher than the exposure dose in evening. But in four modes there were not significant differences between different periods of the day (P>0.05).7. Walking exposure concentration is lower in the roadway of presence of greening in roadside than the no greening rodeside (P<0.001), while no significance difference between greening or not for the bicycle commuter exposure (P>0.05). Bus exposure concentration for waiting on the platform (42.3±31.3μg/m3)> walking to the platform stage (39.6±33.6μg/m3)> in-vehicle exposure concentration (25.2±22.7μg/m3). Subway exposure concentration for walking to the platform stage (31.1±24.0μg/m3)> waiting on platform (29.3±20.7μg/m3)> in-vehicle exposure concentration (25.3±14.4μg/m3), with significant differences between segments of bus and subway commutting. Traffic flow, pedestrian flow, and the number of traffic light stops there were no influences on walking and cycling exposure; number of passenger, seating or standing, near the door or not during bus and subway commute with no significant effect on commutting exposure; four commuter process roundtrip exposure had no statistical difference (P> 0.05).8. Temperature and rainfall had significant negative correlation with four kinds of commuter exposure. Walking, bicycle, bus, subway exposure concentration of PM2.5 concentrations were positively correlated with the environment, the correlation was 0.926,0.932,0.875,0.902, respectively. The correlation to AQI index was 0.895,0.895, 0.838,0.895, respectively. Multivariate regression analysis found that walking commutting exposure to PM2.5 were mainly effected by environmental PM2.5 concentration and season; bicycle commutting exposure to PM2.5 were mainly effected by environmental PM2.5 concentration, temperature and season; bus commutting exposure to PM2.5 were mainly effected by environmental PM2.5 concentration, temperature, season and the day in week; while PM2.5 exposure concentration during the subway commuting were affected by environmental PM2.5 concentration, temperature and relative humidity.Conclusion1. The process of walking, riding a bicycle exposure concentration of PM2.5 was obviously higher than that of bus and subway. The exposure dose of PM2.5 for walking was higher than bicycle, which was followed by bus and subway. One-way exposure dose of bicycle is about 4.5 times of bus one-way exposure dose, and 9 times of the subway. Four commuter exposure to PM2.5 has obvious seasonality, which was lower in summer and autumn, and higher in winter and spring. Weekly variation was also obvious, the exposure concentration on Monday and Friday were significantly lower than that on Wednesday and Sunday.2. Walking exposure concentration is lower in the roadway of presence of greening in roadside than roadway without greening. Bus exposure concentration for waiting on the platform is followed by walking to the platform stage, the exposure concentration of in-vehicle is the lowest. Subway exposure concentration for walking to the platform stage is followed by waiting on platform, the exposure concentration of in-vehicle is the lowest. Monitoring PM2.5 exposure concentration, temperature, seasonal factors are the main factors influencing the commuter PM2.5 exposure. |
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