Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015

doi:10.1007/s11442-019-1595-0

Abstract

Abstract:

High concentrations of PM_2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_2.5 concentrations for regional air quality control and management. In this study, PM_2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM_2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM_2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m³. (2) PM_2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM_2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_2.5. (3) The center of gravity of PM_2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM_2.5 concentrations have moved eastward, while low-value PM_2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The “High-High” PM_2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The “Low-Low” PM_2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM_2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM_2.5 concentration in China.

Key words: air pollution, PM_2.5, haze, spatio-temporal evolution, environmental influence, China

Liang ZHOU, Chenghu ZHOU, Fan YANG, Lei CHE, Bo WANG, Dongqi SUN. Spatio-temporal evolution and the influencing factors of PM_2.5 in China between 2000 and 2015[J].Journal of Geographical Sciences, 2019, 29(2): 253-270.

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References 47

[1]	Austin E, Coull B A, Zanobetti A et al., 2013. A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition.Environment International, 59(3): 244-254. doi: 10.1016/j.envint.2013.06.003 pmid: 3878877
[2]	Beckerman B S, Jerrett M, Serre M et al., 2013. A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States.Environmental Science & Technology, 47(13): 7233-7241. doi: 10.1021/es400039u pmid: 23701364
[3]	Bell M L, Dominici F, Ebisu K et al., 2007. Spatial and temporal variation in PM2.5 chemical composition in the United States for health effects studies.Environmental Health Perspectives, 115(7): 989-995. doi: 10.1289/ehp.9621 pmid: 17637911
[4]	Cao G L, Zhang X Y, Gong S L et al., 2011. Emission inventories of primary particles and pollutant gases for China.Atmospheric Environment, 45(37): 6802-6811. (in Chinese) doi: 10.1016/j.atmosenv.2011.03.068
[5]	Charron A, Harrison R M, 2005. Fine (PM2.5) and coarse (PM2.5-10) particulate matter on a heavily trafficked London highway: Sources and processes.Environmental Science & Technology, 39(20): 7768-7776.
[6]	Cheng S, Yang L X, Zhou X et al., 2011. Evaluating PM2.5 ionic components and source apportionment in Jinan, China from 2004 to 2008 using trajectory statistical methods.Journal of Environmental Monitoring, 13(6): 1662-1671. doi: 10.1039/c0em00756k
[7]	Chow J C, Chen L W, Watson J G et al., 2006. PM2.5 chemical composition and spatiotemporal variability during the California regional PM10/PM2.5 air quality study (CRPAQS).Journal of Geophysical Research Atmospheres, 111(D10): 1-17. doi: 10.1029/2005JD006457
[8]	Chu H J, Huang B, Lin C Y, 2015. Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship.Atmospheric Environment, 102(2): 176-182. doi: 10.1016/j.atmosenv.2014.11.062
[9]	Delfino R J, Sioutas C, Malik S, 2005. Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health.Environmental Health Perspectives, 113(8): 934-946. doi: 10.1289/ehp.7938 pmid: 16079061
[10]	Dockery D W, Pope CA, Xu X et al., 1994. An association between air pollution and mortality in six US cities.New England Journal of Medicine, 329(24): 1753-1759.
[11]	Franklin M, Koutrakis P, Schwartz P, 2008. The role of particle composition on the association between PM2.5 and mortality.Epidemiology, 19(5): 680-689. doi: 10.1097/EDE.0b013e3181812bb7 pmid: 3755878
[12]	Gao M, Cao J, Seto E. A, 2015. A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi’an, China.Environmental Pollution, 199(4): 56-65. doi: 10.1016/j.envpol.2015.01.013 pmid: 25618367
[13]	Gelencsér A, May B, Simpson D et al., 2007. Source apportionment of PM2.5 organic aerosol over Europe: Primary/secondary, natural/anthropogenic, and fossil/biogenic origin.Journal of Geophysical Research Atmospheres, 112(D23): 1-12. doi: 10.1029/2006JD008094
[14]	Gramsch E, Cereceda-Balic F, Oyola P et al., 2006. Examination of pollution trends in Santiago de Chile with cluster analysis of PM10 and ozone data.Atmospheric Environment, 40(28): 5464-5475. doi: 10.1016/j.atmosenv.2006.03.062
[15]	Guo J P, Zhang X Y, Wu Y R et al., 2011. Spatio-temporal variation trends of satellite-based aerosol optical depth in China during 1980-2008. Atmospheric Environment, 45(37): 6802-6811. doi: 10.1016/j.atmosenv.2011.03.068
[16]	Henderson S B, Beckerman B, Jerrett M et al., 2007. Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter.Environmental Science & Technology, 41(7): 2422-2428.
[17]	Hoek G, Brunekreef B, Goldbohm S et al., 2002. Association between mortality and indicators of traffic-related air pollution in the Netherlands: A cohort study.The Lancet, 360(9341): 1203-1209. doi: 10.1016/S0140-6736(02)11280-3 pmid: 12401246
[18]	Huang, Y, Yan Q, Zhang C, 2018. Spatial-temporal distribution characteristics of PM2.5 in China in 2016,Journal of Geovisualization and Spatial Analysis, 2(2): 1-12. doi: 10.1007/s41651-017-0008-0
[19]	Hueglin C, Gehrig R, Baltensperger U et al., 2005. Chemical characterisation of PM2.5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland.Atmospheric Environment, 39(4): 637-651. doi: 10.1016/j.atmosenv.2004.10.027
[20]	Jiang Y A, Chen Y, Zhao Y Z et al., 2013. Analysis on changes of basic climatic elements and extreme events in Xinjiang, China during 1961-2010.Advances in Climate Change Research, 4(1): 20-29. doi: 10.3724/SP.J.1248.2013.020
[21]	Kioumourtzoglou M A, Schwartz J, Weisskopf M et al., 2016. Long-term PM2.5 exposure and neurological hospital admissions in the Northeastern United States.Environmental Health Perspectives, 124(1): 23-29. doi: 10.1289/ehp.1408973 pmid: 25978701
[22]	Kloog I, Nordio F, Coull B et al., 2012. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states.Environmental Science & Technology, 46(21): 11913-11921. doi: 10.1021/es302673e pmid: 4780577
[23]	Laden F, Neas L M, Dockery D W et al., 2000. Association of fine particulate matter from different sources with daily mortality in six US cities.Environmental Health Perspectives, 108(10): 941-947. doi: 10.1289/ehp.00108941
[24]	Laden F, Schwartz J, Speizer F E et al., 2006. Reduction in fine particulate air pollution and mortality.American Journal of Respiratory and Critical Care Medicine, 173(6): 667-672. doi: 10.1164/rccm.200503-443OC
[25]	Lindner A, Pitombo C S, 2018. A conjoint approach of spatial statistics and a traditional method for travel mode choice issues.Journal of Geovisualization and Spatial Analysis, 2(1): 1-13. doi: 10.1007/s41651-017-0008-0
[26]	Lin G, Fu J, Jiang D et al., 2013. Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China.International Journal of Environmental Research and Public Health, 11(1): 173-186. doi: 10.3390/ijerph110100173 pmid: 3924439
[27]	Liu Y, Paciorek C J, Koutrakis P et al., 2009. Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information.Environmental Health Perspectives, 117(6): 886-892. doi: 10.1289/ehp.0800123
[28]	Liu Y, Sarnat JA, Kilaru V et al., 2005. Estimating ground-level PM2.5 in the eastern using satellite remote sensing.Environmental Science & Technology, 39(9): 3269-3278. doi: 10.1021/es049352m pmid: 15926578
[29]	Liu Y S, Yang R, 2012. The spatial characteristics and formation mechanism of the county urbanization in China.Acta Geographica Sinica, 67(8): 1011-1020. (in Chinese) doi: 10.11821/xb201208001
[30]	Lu B, Kong S F, Han Bin, 2011. Inventory of atmospheric pollutants discharged from biomass burning in China continent in 2007.China Environmental Science, 31(2): 186-194. (in Chinese) doi: 10.3724/SP.J.1077.2011.00311
[31]	Merbitz H, Buttstädt M, Michael Set al., 2012. GIS-based identification of spatial variables enhancing heat and poor air quality in urban areas. Applied Geography, 2012, 33(4): 94-106. doi: 10.1016/j.apgeog.2011.06.008
[32]	Pope C A, 2000. Review: Epidemiological basis for particulate air pollution health standards.Aerosol Science & Technology, 32(1): 4-14. doi: 10.1080/027868200303885
[33]	Pope C A, Burnett R T, Thun M J et al., 2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution.Jama, 287(9): 1132-1141. doi: 10.1001/jama.287.9.1132 pmid: 11879110
[34]	Pope C A, Dockery D W, Schwartz J, 1995. Review of epidemiological evidence of health effects of particulate air pollution.Inhalation Toxicology, 7(1): 1-18. doi: 10.3109/08958379509014267
[35]	Samet J M, Dominici F, Curriero F C et al., 2000. Fine particulate air pollution and mortality in 20 U.S cities, 1987-1994. New England Journal of Medicine, 343:(24): 1742-1749. doi: 10.1056/NEJM200012143432401 pmid: 11114312
[36]	Stone B, 2008. Urban sprawl and air quality in large US cities.Journal of Environmental Management, 86(4): 688-698. doi: 10.1016/j.jenvman.2006.12.034 pmid: 17368703
[37]	Wang H, Dwyer-Lindgren L, Lofgren K T et al., 2012. Age specific and sex-specific mortality in 187 countries, 1970-2010: A systematic analysis for the global burden of disease study 2010.The Lancet, 380(9859): 2071-2094. doi: 10.1016/S0140-6736(12)61719-X pmid: 23245603
[38]	Wang J, Christopher S A, 2003. Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies.Geophysical Research Letters, 30(21): 1-4. doi: 10.1029/2003GL018174
[39]	Wang J F, Li X H, George Christakos et al., 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China.International Journal of Geographical Information Science, 24(1): 107-127. doi: 10.1080/13658810802443457
[40]	Wang Z B, Fang C L, Xu G et al., 2015. Spatial-temporal characteristics of the PM2.5 in China in 2014.Acta Geographica Sinica, 70(11): 1720-1734. (in Chinese) doi: 10.11821/dlxb201511003
[41]	Wu D, 2012. Hazy weather research in China in the last decade: A review.Acta Scientiae Circumstantiae, 32(2): 257-269.
[42]	Xu W, He F, Li H et al., 2014. Spatial and temporal variations of PM2.5 in the Pearl River Delta.Research of Environmental Sciences, 27(9): 951-957. doi: 10.13198/j.issn.1001-6929.2014.09.02
[43]	Xue W, Wu W, Fu F et al., 2015. Satellite retrieval of a heavy pollution process in January 2013 in China. Environmental Science, 36, (3): 794-800. (in Chinese) doi: 10.13227/j.hjkx.2015.03.006 pmid: 25929043
[44]	Xue W B, Fu F, Wang J N et al., 2014. Numerical study on the characteristics of regional transport of PM2.5 in China.China Environmental Science, 34(6): 1361-1368. (in Chinese)
[45]	Yi H, Hao J, Tang X L et al., 2007. Atmospheric environmental protection in China: Current status, developmental.Energy Policy, 35(2): 907-915. doi: 10.1016/j.enpol.2006.01.019
[46]	Zhang Y, Cao F, 2015. Fine particulate matter (PM2.5) in China at a city level.Scientific Reports, 5: 1-11. doi: 10.9734/JSRR
[47]	Zhang Y, Zhang W, Wang J et al., 2015. Establishment and application of pollutant inventory-chemical mass balance (I-CMB) model for source apportionment of PM2.5.Transactions of Atmospheric Sciences, 38(2): 279-284. (in Chinese)

No.	Change type	Quantity	%	No.	Change type	Quantity	%
1	D—D—D—D	4	0.17	9	R—D—D—D	38	1.59
2	D—D—D—R	5	0.21	10	R—D—D—R	71	2.97
3	D—D—R—D	11	0.46	11	R—D—R—D	120	5.02
4	D—D—R—R	1	0.04	12	R—D—R—R	68	2.85
5	D—R—D—D	45	1.88	13	R—R—D—D	811	33.93
6	D—R—D—R	13	0.54	14	R—R—D—R	801	33.51
7	D—R—R—D	36	1.51	15	R—R—R—D	190	7.95
8	D—R—R—R	5	0.21	16	R—R—R—R	171	7.15

Detection indices	2000		2006		2011
Detection indices	P	Q	P	Q	P	Q
Natural geographical regionalization (X₁)	0.7047	0.0000	0.7447	0.0000	0.7196	0.0000
Per capita GDP (X₂)	0.0077	0.9191	0.0062	0.9079	0.0068	0.8659
Population density (X₃)	0.4320	0.0000	0.4372	0.0000	0.4120	0.0000
Proportion of the secondary industry (X₄)	0.0984	0.0000	0.0665	0.0031	0.0917	0.0000
Proportion of built-up areas (X₅)	0.0853	0.0030	0.0753	0.1033	0.1025	0.0282
Urban greening ratio (X₆)	0.0280	0.1503	0.0625	0.0319	0.0359	0.1083
Urban residents’ car ownership (X₇)	0.0259	0.8637	0.0913	0.0226	0.1074	0.0080
Sown area (X₈)	0.1396	0.0557	0.1487	0.0000	0.1046	0.0000
Industrial flue dust discharge (X₉)	0.0709	0.1537	0.0936	0.0000	0.0531	0.2766
Energy consumption intensity of lands (X₁₀)	0.3109	0.0000	0.4124	0.0000	0.4143	0.0000
Average iron and steel output of lands (X₁₁)	0.2869	0.0000	0.3373	0.0000	0.3217	0.0000