Fine particulate (PM2.5) dynamics during rapid urbanization in Beijing, 1973–2013

PM2.5 has been given special concern in recent years when the air quality monitoring station started recording. However, long-term PM2.5 concentration dynamic analysis cannot be taken with the limited observations. We therefore estimated the PM2.5 concentration using meteorological visibility data in Beijing. We found that 71 ± 17% of PM10 were PM2.5, which contributed to visibility impairment (y = 332.26e−0.232x; R2 = 0.75, P < 0.05). We then reconstructed a time series of annual PM2.5 from 1973 to 2013, and examined its relationship with urbanization by indicators of population, gross domestic production (GDP), energy consumption, and number of vehicles. Concluded that 1) Meteorological conditions were not the major cause of PM2.5 increase from 1973 to 2013; 2) With population and GDP growth, PM2.5 increased significantly (R2 = 0.5917, P < 0.05; R2 = 0.5426, P < 0.05); 3) Intensive human activity could change air quality in a short period, as observed changes in the correlations of PM2.5 concentration with energy consumption and number of vehicles before and after 2004, respectively. The success of this research provides an easy way in reconstructing long-term PM2.5 concentration with limited PM2.5 observation and meteorological visibility, and insight the impact of urbanization on air quality.

Scientific RepoRts | 6:23604 | DOI: 10.1038/srep23604 2013 (Fig. 2). Moreover, annual mean visibility on days with only wind speeds greater than 4 m/s (V_WS4) were greater than other conditions, indicating strong wind is the major force to remove the air pollutants.
The annual mean PM 2.5 concentration under stable meteorological condition increased significantly (R 2 = 0.6325, P < 0.05; Fig. 3), with wind speed showed a "U-shape" trend which is relative stable, thus, indicated human activities would be the major reason that result in the increase of PM 2.5 concentration (Fig. 3). The seasonal mean increase of PM 2.5 concentration was increased stronger in summer (slope = 1.0269) and autumn (slope = 0.9614) than that in spring (slope = 0.5282) and winter (slope = 0.2342). Moreover, PM 2.5 concentration increased largest in summer, but no significant trend was observed in winter during 1973-2013.
Urbanization indicators were significantly correlated with PM 2.5 concentration at Beijing. Both population (R 2 = 0.5917, P < 0.05; Fig. 4A) and GDP (R 2 = 0.5426, P < 0.05; Fig. 4B) were positively correlated with PM 2.5 concentration during 1973-2013, indicating the increasing human activities is highly attribute to the increase of PM 2.5 concentration. Energy consumption also could contribute to the increase the PM 2.5 concentration (Fig. 4C). The slopes between PM 2.5 concentration and energy consumption were changed after 2004. While, similar correlation was also obtained between PM 2.5 concentration and vehicle amount before and after 2004 (Fig. 4D). PM 2.5 is an important component in PM 10 . However, the ratio of PM 2.5 to PM 10 varies among different areas, for example, 33% in Jeddah City, Saudi Arabia, and between 45-60% in Greece 10-12 . PM 2.5 can easily enter the human respiratory system and cause serious health impacts, while larger particles are not able to penetrate as deeply and therefore cause less serious health impacts 6 . Thus, at the same particulate pollution levels, higher ratios of PM 2.5 to PM 10 indicate the potential for greater negative impacts on human health. In the present study, the ratio in Beijing was found to be 71% ± 17%, indicating the probability of significant impact on health. Furthermore,  V_Original is the original annual mean visibility; V_WS4 is annual mean visibility on days with average wind speeds > 4 m/s; V_FRS is annual mean visibility on days with fog, rain, or snow; V_D_WS4 is annual mean visibility with wind speed (> 4 m/s) days eliminated; V_D_FRS is annual mean visibility, with fog, rain, or snow days eliminated; V_D_WS4_FRS is annual mean visibility, with fog, rain, snow, or wind speed (> 4 m/s) days eliminated.

Discussion
both PM 10 and PM 2.5 are the major course of visibility impairment. If PM 2.5 is not the major component in PM 10 , our method cannot be applied, thus the accuracy of long-term PM 2.5 concentration is highly correlated with the consistency of the correlation between PM 2.5 concentration and visibility during the study period. The particulate data collected in this research was only available for a year, and further calibration of the ratio and the relationship between PM 2.5 concentration and visibility at longer time scale is strongly suggested to improve the accuracy in determining long-term PM 2.5 dynamics at different cities.
The negative impacts of urbanization on the environment, especially on air, have been given special attention in recent years. For instance, the Environmental Kuznets Curve (EKC) found an inverse U-type relationship between the urban eco-environment and the economy, with the turning point of the U-curve normally at a per capita income of $8000. However, we did not observe an inverse U-type relationship between the economy and PM 2.5 concentration, indicating that Beijing may not have reached the turning point in the EKC U-type curve. The relationship between energy consumption, the number of vehicles, and PM 2.5 concentration (Fig. 4C,D) also indicated that the economy was not the only influence on the air environment. Different relationships were observed before and after 2004, for example, indicating the strong impact of human activity on environmental improvement.
Urban systems are not naturally developed, but are always influenced by human activities 1 . Intense human activity can change the urban environment over a short period. This was also observed in this work as the relationship between PM 2.5 and urbanization indicators showed. At beginning, Beijing's development was highly depended on heavy industries that made the GDP increase while polluted the atmospheric environment, however, Similar to other mega cities in China, Beijing will continue its rapid urbanization for another decade as part of the National New-type Urbanization Plan stratagem (2014 to 2020) designed by the Chinese Central Government. From now until 2020, the national urbanization rate is planned to reach around 60% on the basis of the 52.6% achieved in 2012. Such rapid increase will bring more intensive social and economic activities, which will directly affect the urban environment. Thus, the development of better strategies for the control and reduction of air pollution without compromising economic growth is essential for China's continued urbanization.

Materials and Methods
Daily visibility and meteorological data. Daily visibility, wind speed at 10 m height, and indicators for occurrences of fog, rain, and snow were obtained from Global Summary of the Day from the National Climate Data Center of the U.S. Department of Commerce. These data have been recorded in Beijing since 1973, allowing long-term series analysis of visibility in order to illustrate particulate pollution dynamics in the city.
Social-economic data. Data on the annual urban population, gross domestic production (GDP), energy consumption, and numbers of vehicles in Beijing were collected from the Beijing 60 Yearbook, and were further correlated with the annual PM 2.5 dynamics to understand the impact of urbanization on urban air quality in a typical Chinese megacity.

Visibility under stable meteorological condition. Visibility under stable meteorological condition
could illustrate the local particulate pollution condition, we therefore eliminate the visibility under instable meteorological conditions: (1) visibility under rain, fog, and snow days was firstly eliminated to minimize visibility impairment from natural precipitation; (2) and then, visibility with wind speed faster than 4 m/s, which was deduced in our previous research when comparing wind speed with air quality index (AQI) 4 , was also eliminated Estimation of annual PM 2.5 concentration from visibility. The relationship between PM 2.5 and PM 10 was firstly examined to ensure that PM 2.5 was the major component in PM 10 that caused the visibility impairment. The correlation between daily PM 2.5 concentration and visibility was then obtained under stable meteorological conditions. From this, 40 years of PM 2.5 concentration dynamics were finally estimated. Correlation analysis. Annual and seasonal stable PM 2.5 concentrations were firstly correlated with annual and seasonal stable wind speeds during the 40 years to understand stable meteorological conditions has less impact on local emitted PM 2.5 dynamics. Correlations between PM 2.5 and population, GDP, energy consumption, and number of vehicles were then examined to understand the impact of urbanization on PM 2.5 concentrations in the typical Chinese megacity, Beijing.