Abstract
PM2.5 has become an increasing public concern recently because of its visibility reduction and severe health risks. For the whole year of 2013, hourly PM2.5 data of 496 monitoring sites scattered in 74 cities of China are collected to analyze temporal and spatial variability of PM2.5 concentration. Different temporal scales (seasonal variation, monthly variation and daily variation) and spatial scales (urban versus rural, typical areas and national scale) are discussed. Results show that PM2.5 concentration changes significantly in both long-term and short-term scales. An apparent bimodal pattern exists in daily variation of PM2.5 concentration and the daytime peak appears around 10:00 am while the lowest concentration appears around 16:00 pm. Spatial autocorrelation analysis and Ordinary Kriging are used to characterize spatial variability. Moran’s I of PM2.5 concentration in three typical regions, the Beijing-Tianjin-Hebei region, the Yangtze River Delta region and the Pearl River Delta region, is 0.906, 0.693, 0.746, respectively, which indicates that PM2.5 is strong spatial correlated. Spatial distribution of annual PM2.5 concentration simulated by Ordinary Kriging shows that 7.94 million km2 (83%) areas fail in meeting the requirement of China’s National Ambient Air Quality Standards Level-2 (35 µg/m3) and there are at least three concentrated highly polluted areas across the country.
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He K B, Huo H, Zhang Q. Urban air pollution in China: Current status, characteristics, and progress [J]. Annual Review of Energy and the Environment, 2002, 27: 397–431.
Chan C K, Yao X H. Air pollution in mega cities in China [J]. Atmospheric Environment, 2008, 42(1): 1–42.
Chen Y, Ebenstein A, Greenstone M, et al. Evidence on the impact of sustained exposure to air pollution on life
expectancy from China’s Huai River policy [J]. Proc Natl Acad Sci USA, 2013, 110(32): 12936-12941.
Zhang Y Q, He M Q, Wu S M, et al. Short-term effects of fine particulate matter and temperature on lung function among healthy college students in Wuhan, China[J]. International Journal of Environmental Research and Public Health, 2015,12(7):7777–7793.
Brunekreef B, Holgate S T. Air pollution and health [J]. The Lancet, 2002, 360(9341): 1233–1242.
Gehrig R, Buchmann B. Characterising seasonal variations and spatial distribution of ambient PM10 and PM2.5 concentrations based on long-term Swiss monitoring data [J]. Atmospheric Environment, 2003, 37(19): 2571–2580.
Xu G, Jiao L M, Zhao S, et al. Examining the impacts of land use on air quality from a spatio-temporal perspective in Wuhan, China [J]. Atmosphere, 2016, 7(5): 62.
Russell M, Allen D T, Collins D R, et al. Daily, seasonal and spatial trends in PM2.5 mass and composition in southeast Texas special issue of aerosol science and technologyon findings from the fine particulate matter supersites program [J]. Aerosol Science and Technology, 2004, 38: 14–26.
Hasheminassab S, Pakbin P, Delfino R J, et al. Diurnal and seasonal trends in the apparent density of ambient fine and coarse particles in Los Angeles [J]. Environ Pollut, 2014, 187: 1–9.
Pitz M, Schmid O, Heinrich J, et al. Seasonal and diurnal variation of PM2.5 apparent particle density in urban air in Augsburg,Germany [J]. Environmental Science &Technology, 2008, 42(14): 5087–5093.
Jiao L, Xu G, Zhao S, et al. LUR-based simulation of the spatial distribution of PM2.5 of Wuhan [J]. Geomatics and Information Science of Wuhan University, 2015, 40(8): 1088–1094(Ch).
Hoek G, Beelen R, de Hoogh K, et al. A review of land-use regression models to assess spatial variation of outdoor air pollution [J]. Atmospheric Environment, 2008, 42(33): 7561–7578.
Zou B, Pu Q, Bilal M, et al. High-resolution satellite mapping of fine particulates based on geographically weighted regression [J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(4): 495–499.
Carrat F, Valleron A J. Epidemiologic mapping using the “Kriging” method: Application to an influenza-like epidemic in France [J]. American Journal of Epidemiology, 1992, 135(11): 1293–1300.
Zou B, Zhan F B, Zeng Y, et al. Performance of Kriging and EWPM for relative air pollution exposure risk assessment [J]. International Journal of Environmental Research, 2011, 5(3): 769–778.
Liu G, Bi R, Wang S, et al. The use of spatial autocorrelation analysis to identify PAHs pollution hotspots at an industrially contaminated site [J]. Environ Monit Assess, 2013, 185(11): 9549–9558.
Zhao X J, Zhang X L, Xu X F, et al. Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing [J]. Atmospheric Environment, 2009, 43(18): 2893–2900.
Hu J L, Wang Y G, Ying Q, et al. Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta, China [J]. Atmospheric Environment, 2014, 95: 598–609.
Tobler W R. A computer movie simulating urban growth in the Detroit region [J]. Economic Geography, 1970, 46: 234–240.
Moran P A P. Notes on continuous stochastic phenomena [J]. Biometrika, 1950, 37(1/2): 17–23.
Geary R C. The contiguity ratio and statistical mapping [J]. The Incorporated Statistician, 1954, 5(3): 115–146.
Cliff A D. Spatial processes: Models & applications [M. London: Pion, 1981.
Anselin L. Local indicators of spatial association—LISA [J]. Geographical Analysis, 1995, 27(2): 93–115.
Ross Z, Jerrett M, Ito K, et al. A land use regression for predicting fine particulate matter concentrations in the New York City region [J]. Atmospheric Environment, 2007, 41(11): 2255–2269.
Matheron G. The Theory of Regionalized Variables and Its Applications [D]. Paris: École national supérieure des mines, 1971.
Krige D. A Statistical Approach to Some Mine Valuations and Allied Problems at the Witwatersrand [D]. Witwatersrand: University of Witwatersrand, 1951.
Bayraktar H, Turalioglu F S. A Kriging-based approach for locating a sampling site—In the assessment of air quality [J]. Stochastic Environmental Research and Risk Assessment, 2005, 19(4): 301–305.
Vicedo-Cabrera A M, Biggeri A, Grisotto L, et al. A Bayesian Kriging model for estimating residential exposure to air pollution of children living in a high-risk area in Italy [J]. Geospatial Health, 2013, 8(1): 87–95.
Liao D P, Peuquet D J, Duan Y K, et al. GIS approaches for the estimation of residential-level ambient PM concentrations [J]. Environmental Health Perspectives, 2006, 114(9): 1374–1380.
Lü C, Tian H. Spatial and temporal patterns of nitrogen deposition in China: Synthesis of observational data [J]. Journal of Geophysical Research, 2007, 112: D22S05.
Ma Z, Hu X, Huang L, et al. Estimating ground-level PM2.5 in China using satellite remote sensing [J]. Environ Sci Technol, 2014, 48(13): 7436–7444.
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Foundation item: Supported by the National Natural Science Foundation of China (41571385).
Biography: XU Gang, male, Ph.D. candidate, research direction: air pollution geo-science modeling.
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Xu, G., Jiao, L., Zhao, S. et al. Spatial and temporal variability of PM2.5 concentration in China. Wuhan Univ. J. Nat. Sci. 21, 358–368 (2016). https://doi.org/10.1007/s11859-016-1182-5
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DOI: https://doi.org/10.1007/s11859-016-1182-5