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Atmospheric Pollution Research(8(6),2017).Pdf Atmospheric Pollution Research 8 (2017) 1151e1159 HOSTED BY Contents lists available at ScienceDirect Atmospheric Pollution Research journal homepage: http://www.journals.elsevier.com/locate/apr Spatio-temporal variation and influence factors of PM2.5 concentrations in China from 1998 to 2014 * * Debin Lu a, b, , Jianhua Xu a, , Dongyang Yang a, Jianan Zhao a a School of Geographic Sciences, East China Normal University, Shanghai, 200241, China b Department of Tourism and Geography, Tongren University, Tongren, Guizhou Province, 554300, China article info abstract Article history: Based on the remote sensing retrieval of PM2.5 concentration data in the long-time series, both the linear Received 17 March 2017 regression and grey system correlation analysis methods were employed to analyze the spatial and Received in revised form temporal pattern, variation trend and the main influencing factors of PM2.5 concentration in China from 15 May 2017 1998 to 2014. The results showed that only 16.21%e24.67% of the land area in China PM concentrations Accepted 21 May 2017 2.5 reached the annual average criterion value of 10 mg/m3 set by the World Health Organization (WHO) in Available online 27 May 2017 3 1998e2014; the PM2.5 concentrations were greater than 95 mg/m mainly in Xinjiang Taklimakan Desert, west of Tianjin and the central region of Hebei. PM concentration was less than 10 mg/m3 mainly in Keywords: 2.5 Tibet, western Sichuan, northeastern Yunnan, Taiwan, northern Xinjiang, northern Inner Mongolia and PM2.5 pollution Spatio-temporal variation northwest of Heilongjiang. High PM2.5 concentration in the northwest of China was mainly affected by Grey correlation analysis sand and dust, while it was mainly caused by human activities in the eastern region. Except for Taiwan, Influence factors low PM2.5 concentration areas were mainly located in the economically backward regions. The positive China indicators in highly correlation with PM2.5 concentration include the average temperature, the propor- tion of primary and secondary industry to GDP, industrial consumption, the proportion of fulfilled amount of investment in real estate development to GDP, SO2 emissions and population density. The negative indicators in highly correlation with PM2.5 concentration include the average precipitation, the average wind velocity, the proportion of the tertiary industry to GDP, and the greening coverage rate of the built-up areas. © 2017 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved. 1. Introduction has also become a social concern and the focus of public opinion and academic research. With the rapid development of economy and urbanization in At present, the ground observation (Wang et al., 2014a,b; Zhang China, a series of environmental problems have been brought and Cao, 2015; Sun et al., 2016) and satellite remote sensing about, especially the pollution of PM2.5 (particulate matter with retrieval of data (Han et al., 2015; Zheng et al., 2016; Lin et al., 2013; aerodynamic2.5 mg/m3) (Geng et al., 2015; Tian et al., 2014; Wang Peng et al., 2016) are applied for the study of PM2.5 concentration. et al., 2014a,b) has seriously affected the human health (Guan et al., The accuracy and time resolution of ground observation data are 2014) and socio-economic development (Xie et al., 2016). It has high, but the data from monitoring stations often cannot reflect the become a serious obstacle to the sustainable development of Chi- spatial variability of PM2.5 concentration in the large area, espe- nese cities, forcing the government to take control measures such cially in the mountainous areas with complex topography and as traffic restrictions (Wang et al., 2015), class suspension for stu- highly populated cities with complicated underlying surface. The dents, factory production limitation, or even stopping production. It mean value of the data from the decentralized monitoring stations as the PM2.5 concentration in the area may lead to systematic errors in assessing population exposure and health effects (Lin et al., * Corresponding authors. School of Geographic Sciences, East China Normal 2016). Compared with the ground observation, the satellite University, Shanghai, 200241, China. remote sensing can quickly obtain PM2.5 concentration of contin- E-mail addresses: [email protected] (D. Lu), [email protected] (J. Xu). uous surface in the large area, especially it has the vital significance Peer review under responsibility of Turkish National Committee for Air Pollu- tion Research and Control. for understanding the PM2.5 pollution level in areas where the http://dx.doi.org/10.1016/j.apr.2017.05.005 1309-1042/© 2017 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved. 1152 D. Lu et al. / Atmospheric Pollution Research 8 (2017) 1151e1159 monitoring stations have not been established. By the end of 2012, sources, urbanization and industrial structure, corporate pollution the national air pollution monitoring network has been established, control and technological progress, according to data availability but few monitoring stations, short construction time and lack of principle as well as its pollution sources and influencing fac- long-term observation data have been shown. Therefore, the sat- tors(He et al., 2016). Meteorological conditions include precipi- ellite remote sensing retrieval of data is still an effective means to tation, average wind velocity, average temperature and average evaluate the PM2.5 pollution level in the large area (Ma et al., 2015), relative humidity to reflect the influence on PM2.5 mass concen- especially for the variation trend analysis and evaluation of the tration; PM2.5 pollution source indicators include industrial elec- PM2.5 concentration, it is indispensable. The research on PM2.5 tricity consumption, proportion of fulfilled amount of investment concentration both at home and abroad mainly focused on element in real estate development to GDP, sulfur dioxide emissions, composition (Shaltout et al., 2013), source analysis (Huang et al., smoke (dust) emissions, the total volume of annual bus passenger 2014; Jansen et al., 2014), health effect (Song et al., 2016), pollu- traffic, total road freight and public transport vehicles, reflecting tion control (Zhang et al., 2013) and so on. Although some scholars the industrial production process and traffic-generated road dust, used the annual average or multi-year average to study PM2.5 exhaust emissions. Urbanization and industrial structure include pollution pattern, there are still relatively fewer studies and anal- population density, area of built-up districts, per capita GDP, ysis on PM2.5 concentration pattern, variation trend and influencing proportions of primary, secondary and tertiary industry to GDP, factors, because the PM2.5 concentration data are difficult for total green area and greening coverage rate of built-up area, to acquisition in a large scale for a long term or the continuous reflect the influence of urbanization process and socio-economic monitoring has not been performed for the data at all. PM2.5 activities on PM2.5 concentration. Corporate pollution control pollution process is a very complex system process, the pollution and technological progress include SO2 removal, smoke (dust) source diversity is remarkable, which contains a large number of removal and proportion of R&D expenditure to GDP, reflecting the known and unknown information state, and it is a typical grey intensity of corporate emission reduction and pollution control system (He et al., 2016). technology. Therefore, based on the remote sensing retrieval of PM2.5 concentration data in the long-time series, data extraction and partition calculation, the spatial pattern of PM2.5 concentration in 2.2. PM2.5 concentrations data China from 1998 to 2014 was analyzed. The linear regression method was employed to reveal the variation trend of PM2.5 Currently, there is a lack of publically available global remote concentration. Finally, the grey correlation analysis method was sensing data relating to PM2.5. van Donkelaar et al., (2006, 2010, fi adopted to analyze the correlation between PM2.5 concentration 2015) estimate ground-level ne particulate matter (PM2.5)by and influencing indicators. Through the above methods, the main combining Aerosol Optical Depth (AOD) retrievals from the NASA factors influencing PM2.5 concentration change can be found in MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem order to make targeted control and improve urban air quality, chemical transport model, and subsequently calibrated to global providing a scientific basis for China to choose a scientificpathof ground-based observations of PM2.5 using Geographically new urbanization development and make policy on PM2.5 pollu- Weighted Regression (GWR), The global PM2.5 concentration tion control. dataset had a spatial resolution of 10 km from 1998 to 2014. It is by far the most accurate PM2.5 remote-sensing data set with the largest coverage and longest time span that is available, it has been 2. Index system construction and data sources validated and can be effectively applied on a national scale (Lee et al., 2012; de Sherbinin et al., 2014). The dataset can be directly 2.1. PM2.5 concentration impact index system construction download from Atmospheric Composition Analysis Group at Dal- housie University (http://fizz.phys.dal.ca/~atmos/martin/?page_ PM2.5 is mainly originated from coal combustion, motor id¼140). We used a subset of global PM2.5 concentration dataset vehicle exhaust, road dust and biomass combustion (Cao, 2014), that covered China from 1998 to 2014 extracted from.nc format by and its formation and changes are also affected by meteorological ArcGIS Soft. conditions and other factors. Therefore, 22 indexes (Table 1)were Although, van Donkelaar et al. has validated the accuracy of the selected from four aspects of meteorological conditions, pollution global annual PM2.5 concentrations grids dataset based on the Table 1 Influence index system of PM2.5 concentration in China.
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