基于灰色关联模型对江苏省PM2.5浓度影响因素的分析

doi:10.11821/dlxb201607003

Abstract

Abstract:

In this paper, the Kriging interpolation method was introduced to analyze the spatial distribution characteristics of PM_2.5 in Jiangsu province in 2014, and then the evaluation index system for the PM_2.5 was constructed, which consists of three index layers and 27 indexes. The grey correlation analysis method was used to explore the correlation between PM_2.5 and its influencing factors. Finally, the relationship between the spatial distribution of PM_2.5 and the main influencing factors was analyzed. The conclusions can be drawn as follows: (1) The PM_2.5 in the coastal areas and the north is lower, while it is higher in the inland areas and the south. (2) The weight of PM_2.5 pollution sources index layer is the largest (w_i = 0.4691), the weight of the air quality index and meteorological elements layer is larger (w_i = 0.2866), and the weight value of urbanization and industrial structure index layer is the minimum (w_i = 0.2453). (3) In the 27 indexes, the volume of highway freight, housing construction area, garden green space area and population density have moderate correlation degrees. The other indexes have strong correlation degrees, among which, the correlation degree of the PM₁₀, O₃, total road freight volume and gross industrial output value are relatively high. (4) The synthetic correlation degree values between the PM_2.5 pollution sources index layer and PM_2.5 are much higher in cities of Nanjing, Wuxi, Changzhou, Nantong and Taizhou. The synthetic correlation degree values between urbanization and industrial structure index layer and PM_2.5 are much higher in cities of Xuzhou, Suzhou, Yancheng and Changzhou. The synthetic correlation degree values between the air quality index and meteorological elements layer and PM_2.5 are much higher in cities of Yancheng, Yangzhou, Changzhou and Nantong. Our results demonstrate that the grey correlation degrees of the evaluation indexes system are closely related with spatial distribution of PM_2.5 in Jiangsu province. Therefore, the grey correlation analysis model can be employed to analyze and evaluate the spatial distribution of PM_2.5.

Key words: grey correlation model, the spatial distribution of PM2.5, influencing index, Jiangsu province

Xiang HE, Zhenshan LIN, Huiyu LIU, Xiangzhen QI. Analysis of the driving factors of PM_2.5 in Jiangsu province based on grey correlation model[J].Acta Geographica Sinica, 2016, 71(7): 1119-1129.

Figures/Tables 8

Tab. 1

Tab. 2

Data of the PM2.5 influencing indices for the 13 provincial cities in Jiangsu province

指标层		因子	南京市	无锡市	徐州市	常州市	苏州市	南通市	连云港市	淮安市	盐城市	扬州市	镇江市	泰州市	宿迁市
PM_2.5		X₀	73.82	67.27	67.28	66.48	65.92	61.51	74.78	68.65	58.30	65.93	67.28	62.83	68.81
空气质量指标及气象要素	大气污染物	X₁	123.46	105.38	118.86	103.76	89.98	93.89	112.03	101.78	92.54	112.29	109.70	110.42	114.88
		X₂	23.00	28.17	37.95	35.16	22.06	26.37	29.68	27.66	19.83	33.06	24.40	30.61	27.11
		X₃	0.90	1.10	1.22	1.10	0.93	0.74	1.01	1.07	0.77	0.83	1.18	1.21	1.20
		X₄	50.82	44.28	37.16	44.57	51.79	36.30	35.28	23.33	25.43	32.22	45.33	22.87	33.18
		X₅	99.43	101.66	91.68	102.48	95.89	105.04	98.83	111.73	101.12	86.79	90.05	75.59	92.01
	气象要素	X₆	29.99	37.99	22.76	33.21	34.84	36.43	23.55	26.92	37.48	30.56	23.50	22.84	28.74
		X₇	25.96	22.01	17.31	20.93	20.76	28.67	20.03	21.39	22.81	18.76	28.08	22.64	17.30
		X₈	165.09	168.78	156.64	157.37	171.46	159.49	147.39	149.43	149.53	163.13	158.22	156.89	150.55
		X₉	73.96	73.36	66.08	66.12	73.61	77.40	75.31	77.32	78.60	72.47	86.75	74.52	73.01
		X₁₀	51.06	47.23	60.83	48.84	44.47	50.00	53.19	47.15	60.08	56.23	58.86	57.60	54.14
PM_2.5 污染物来源	交通	X₁₁	11131	2951	3926	4276	4610	3928. 0	1529	4871	2891	4203	1602	3242	3228
		X₁₂	10493	3364	10259	4673	24010	6586	3133	4420	4018	6035	2245	3437	1374
		X₁₃	10912	4734	7529	6932	5065	4769	3053	2220	2110	6155	3529	1123	762
		X₁₄	140.41	67.63	35.80	61.01	113.22	34.56	12.13	19.30	14.91	29.02	16.15	17.97	14.45
	工业生产	X₁₅	286.71	194.01	158.90	215.48	418.01	96.62	28.35	63.65	24.96	69.70	84.67	53.43	51.01
	工业气体排放	X₁₆	112435	83745	136060	35916	165939	65145	49739	47931	47125	48891	66346	52501	29906
		X₁₇	143701	148750	209055	93502	248636	71126	45952	70366	51645	81567	82025	53889	37826
		X₁₈	68910	47123	58398	36964	70299	36439	23221	23899	32914	19023	24452	18243	40074
	建筑	X₁₉	16637	4431	7703	8765	10999	54420	3451	8054	8943	17652	1905	18977	4842
城市化与产业结构	城市与绿化面积	X₂₀	86117	18333	15269	8088	21315	7271	19852	6170	4040	6724	7388	3585	8300
		X₂₁	15	15	16	13	15	14	14	13	12	17	18	9	13
		X₂₂	44	43	43	43	42	42	40	41	40	43	42	41	42
		X₂₃	713	325	253	186	441	172	150	140	96	132	128	96	75
	人口数量	X₂₄	1243	2194	1043	1813	1224	1532	736	846	869	1048	1131	1032	710
	产业结构	X₂₅	0.43	0.49	0.54	0.52	0.52	0.53	0.50	0.48	0.56	0.53	0.51	0.56	0.51
	产业结构	X₂₆	98011	115985	83828	99368	121230	82054	54815	48405	59043	90355	103675	79930	41390
		X₂₇	12563	5853	5085	7874	11620	3827	1384	2873	2208	5426	2762	3618	1019

Tab. 2

Fig. 1

Fig. 2

Tab. 3

Tab. 4

Grey correlation coefficient matrix of PM2.5 of 13 provincial cities in Jiangsu province

指标体系		南京市	无锡市	徐州市	常州市	苏州市	南通市	连云港市	淮安市	盐城市	扬州市	镇江市	泰州市	宿迁市
指标层		南京市	无锡市	徐州市	常州市	苏州市	南通市	连云港市	淮安市	盐城市	扬州市	镇江市	泰州市	指标因子
空气质量指标与气象要素	X₁	1.0000	0.8093	0.6380	0.8118	0.5046	0.8474	0.5368	0.6137	0.8674	0.7413	0.9793	0.6384	0.8828
	X₂	0.3774	0.8095	0.5429	0.6108	0.5763	0.7643	0.4322	0.6804	1.0000	0.6773	0.6053	0.5127	0.6448
	X₃	0.4288	0.7470	0.5428	0.7107	0.8259	0.7074	0.4202	0.9593	0.9133	0.6184	0.5989	0.6503	0.6473
	X₄	0.9368	0.7547	0.8549	0.6917	0.4955	0.6599	0.7124	0.4344	0.8497	0.7477	0.7160	0.3588	0.6093
	X₅	0.4312	0.6257	0.9539	0.6481	0.9049	0.9586	0.7937	0.4284	0.6299	0.7162	0.9587	0.8246	0.7918
	X₆	0.5130	0.4639	0.5429	0.7006	0.6376	0.8272	0.4096	0.8935	0.9370	0.9917	0.5729	0.8318	0.8769
	X₇	0.6773	0.7537	0.4636	0.7061	0.7285	0.3866	0.9756	0.6192	0.5076	0.5791	0.5747	0.5410	0.4247
	X₈	0.6541	0.6167	0.7200	0.8161	0.4955	0.6279	0.6876	0.4621	0.8484	0.7549	0.7958	0.5006	0.4781
	X₉	0.4469	0.6891	0.4634	0.4876	0.7983	0.5945	0.6950	0.8026	0.4522	0.7323	0.5425	0.5074	0.5939
	X₁₀	0.4559	0.5499	0.5429	0.6577	0.5046	0.7919	0.6204	0.4983	0.3438	0.6876	0.6239	0.8461	0.8536
PM_2.5污染来源	X₁₁	1.0000	0.5378	0.6029	0.6745	0.7463	0.9207	0.6876	0.6105	0.7790	0.7008	0.4669	0.4114	0.4999
	X₁₂	0.4557	0.5051	0.7284	0.5671	0.4955	0.9551	0.7699	0.4843	0.8107	0.6361	0.4808	0.3839	0.4247
	X₁₃	1.0000	0.7283	0.8507	0.8613	0.8819	0.7267	0.9971	0.4886	0.7901	0.9270	0.6205	0.3682	0.4247
	X₁₄	1.0000	0.7749	0.5590	0.7736	0.6272	0.9400	0.6876	0.4500	0.9585	0.5812	0.4775	0.3709	0.4314
	X₁₅	0.5995	0.7719	0.6773	0.9214	0.4955	0.9534	0.6959	0.4679	1.0000	0.5694	0.5397	0.3784	0.4501
	X₁₆	0.5597	0.7330	0.7129	0.5085	0.4955	0.9053	0.8600	0.4833	0.7980	0.5866	0.6169	0.4073	0.4247
	X₁₇	0.5011	0.9063	0.6819	0.6551	0.4955	0.9111	0.7261	0.4937	0.8841	0.6374	0.5755	0.3795	0.4247
	X₁₈	0.9493	0.9559	0.7221	0.7488	0.4955	0.7779	0.7917	0.4724	0.6395	0.5118	0.5213	0.3588	0.6598
	X₁₉	0.4100	0.4855	0.5162	0.5576	0.6115	0.3866	0.7166	0.4762	0.7886	0.7224	0.4636	0.4679	0.4459
城市化与产业结构	X₂₀	0.3333	0.6726	0.6414	0.5446	0.6295	0.4005	0.4648	0.6237	0.3346	0.5155	0.5711	0.8246	0.6530
	X₂₁	0.4450	0.7011	0.5540	0.8915	0.7427	0.7092	0.7061	0.8301	0.4229	0.5358	0.4636	0.8246	0.8163
	X₂₂	0.3333	0.6507	0.6303	0.6742	0.9715	0.6706	0.3928	0.7782	0.3621	0.6430	0.7422	0.9319	0.7132
	X₂₃	1.0000	0.7288	0.6250	0.5859	0.8579	0.9013	0.8202	0.4694	0.9382	0.5540	0.5023	0.3674	0.4247
	X₂₄	0.4384	0.5422	0.5851	0.6982	0.7762	0.5903	0.7049	0.4652	0.8233	0.6546	0.6293	0.4251	0.4247
	X₂₅	0.3333	0.8318	0.6750	0.8051	0.7567	0.4829	0.6161	0.6489	0.3462	0.6503	0.9876	0.8249	0.8100
	X₂₆	0.6323	0.5838	0.9134	0.7155	0.4955	0.6230	0.8944	0.4633	0.6934	0.8047	0.7125	0.5489	0.4247
	X₂₇	1.0000	0.7586	0.6880	0.8828	0.5391	0.9320	0.7188	0.4968	0.8292	0.8194	0.5391	0.4279	0.4247

Tab. 4

Tab. 5

Tab. 6

References 34

[1]	Gehrig R, Buchmann B.Characterising seasonal variations and spatial distribution of ambient PM10 and PM2.5 concentrations based on long-term Swiss monitoring data. Atmospheric Environment, 2003, 37(19): 2571-2580. doi: 10.1016/S1352-2310(03)00221-8
[2]	Song Yu, Tang Xiaoyan, Zhang Yuanhang, et al.Effects on fine particles by the continued high temperature weather in Beijing. Environmental Science, 2002, 23(4): 33-37. doi: 10.3321/j.issn:0250-3301.2002.04.007
	[宋宇, 唐孝炎, 张远航, 等. 夏季持续高温天气对北京市大气细粒子(PM2.5)的影响. 环境科学, 2002, 23(4): 33-37.] doi: 10.3321/j.issn:0250-3301.2002.04.007
[3]	Wei Yuxiang, Yin Yan, Yang Weifen, et al.Analysis of the pollution characteristics influence factors of PM2.5 in Nanjing area. Environmental Science and Management, 2009, 34(9): 29-34. doi: 10.3969/j.issn.1673-1212.2009.09.008
	[魏玉香, 银燕, 杨卫芬, 等. 南京地区PM2.5污染特征及其影响因素分析. 环境科学管理, 2009, 34(9): 29-34.] doi: 10.3969/j.issn.1673-1212.2009.09.008
[4]	Zhao Chenxi, Wang Yunqi, Wang Yujie, et al.Temporal and spatial distribution of PM2.5 and PM10 pollution status and the correlation of particulate matters and meteorological factors during winter and spring in Beijing. Environmental Science, 2014, 35(2): 418-427.
	[赵晨曦, 王云琦, 王玉杰, 等. 北京地区冬春PM2.5和PM10污染水平时空分布及其与气象条件的关系. 环境科学, 2014, 35(2): 418-427.]
[5]	Wei Fusheng, Teng Enjiang, Wu Guoping, et al.Concentrations and elemental components of PM2. 5、PM10 in ambient air in four large Chinese cities. Environmental Monitoring in China, 2001, 17(7): 1-6.
	[魏复盛, 滕恩江, 吴国平, 等. 我国4个大城市空气PM2.5、PM10污染及其化学组成. 中国环境监测, 2001, 17(7): 1-6.]
[6]	Bao Zhen, Feng Yinchang, Jiao Li.Characterization and source apportionment of PM2.5 and PM10 in Hangzhou. Environmental Monitoring in China, 2010, 26(2): 44-48.
	[包贞, 冯银厂, 焦荔. 杭州市大气PM2.5和PM10污染特征及来源解析. 中国环境监测, 2010, 26(2): 44-48.]
[7]	Xiao Zhimei, Bi Xiaohui, Feng Yinchang, et al.Source apportionment of ambient PM10 and PM2.5 in urban area of Ningbo city. Research of Environmental Sciences, 2012, 25(5): 549-555.
	[肖致美, 毕晓辉, 冯银厂, 等. 宁波市环境空气中PM10和PM2.5来源解析. 环境科学研究, 2012, 25(5): 549-555.]
[8]	Dockery D W, Pope C A, Xu X, et al.An association between air pollution and mortality in six US cities. New England Journal of Medicine, 1993, 329(24): 1753-1759. doi: 10.1056/NEJM199312093292401 pmid: 8179653
[9]	Yin Yongwen, Cheng Jinping, Duan Yusen, et al.Correlation analysis between the PM2.5, PM10 which were taken in the hazy day and the number of outpatient about breathing sections, breathing sections of pediatrics in shanghai. Environmental Sciences, 2011, 32(7): 1894-1898.
	[殷永文, 程金平, 段玉森, 等. 上海市霾期间 PM2.5、PM10污染与呼吸科、儿呼吸科门诊人数的相关分析. 环境科学, 2011, 32(7): 1894-1898.]
[10]	Jia Yuqiao, Zhao Xiaohong, Guo Xingbiao.Effects of PM10 and PM2.5 on secretion of inflammatory factors in human lung fibroblast cell. Journal of Environment and Health, 2011, 28(3): 206-208.
	[贾玉巧, 赵晓红, 郭新彪. 大气颗粒物PM10和PM2.5对人肺成纤维细胞及其炎性因子分泌的影响. 环境与健康杂志, 2011, 28(3): 206-208.]
[11]	Wang Wengming, Sheng Huiping, Huang Yunbiao, et al.The PM2.5 fine particulate pollution states and its impact on human health. Shanghai Journal of Preventive Medicine, 2015, 27(5): 290-295.
	[王文朋, 沈惠平, 黄云彪, 等. 大气PM2.5细颗粒物污染状况及对人体健康的影响. 上海预防医学, 2015, 27(5): 290-295.]
[12]	Buseck P R, POsfai M.Airborne minerals and related aerosol particles: effects on climate and the environment. Proceedings of the National Academy of Sciences, 1999, 96(7): 3372-3379. doi: 10.1073/pnas.96.7.3372 pmid: 10097046
[13]	Tsai Y I, Kuoc S C, Lee W J, et al.Long-term visibility trends in one highly urbanized, one highly industrialized, and two rural areas of Taiwan. Science of Total Environment, 2007, 382(2/3): 324-341. doi: 10.1016/j.scitotenv.2007.04.048 pmid: 17544482
[14]	Viana M, Kuhlbusch T A, Querol X, et al.Source apportionment of particulate matter in Europe: A review of methods and results. Journal of Aerosol Science, 2008, 39(10): 827-849. doi: 10.1016/j.jaerosci.2008.05.007
[15]	Zhu Xianlei, Zhang Yuanhang, Zeng Limin, et al.Source identification of ambient PM2.5 in Beijing. Research of Environmental Sciences, 2005, 18(5): 1-5. doi: 10.3321/j.issn:1001-6929.2005.05.001
	[朱先磊, 张远航, 曾立民, 等. 北京市大气细颗粒物PM2.5的来源研究. 环境科学研究, 2005, 18(5): 1-5.] doi: 10.3321/j.issn:1001-6929.2005.05.001
[16]	Cheng Hairong, Wang Zuwu, Feng Jialiang, et al.Carbonaceous species composition and source apportionment of PM2.5 in urban atmosphere of Wuhan. Ecology and Environmental Sciences, 2012, 21(9): 1574-1579.
	[成海容, 王祖武, 冯家良, 等. 武汉市城区大气PM2.5的碳组分与源解析. 生态环境学报, 2012, 21(9): 1574-1579.]
[17]	Zhong Wuya, Yan Wei.Analysis on urban economic development in view of PM2.5. Urban Insight, 2013(1): 169-174. doi: 10.3969/j.issn.1674-7178.2013.01.018
	[钟无涯, 颜玮. 城市经济发展与PM2.5关系探析. 城市观察, 2013(1): 169-174.] doi: 10.3969/j.issn.1674-7178.2013.01.018
[18]	Qi Yuang, Zhang Yongan.Dynamic relationship between the evolution of three industries and PM2.5 emissions in Beijing. China Population, Resources and Environment, 2015, 25(7): 15-23. doi: 10.3969/j.issn.1002-2104.2015.07.003
	[齐园, 张永安. 北京三次产业演变与PM2.5排放的动态关系研究. 中国人口·资源与环境, 2015, 25(7): 15-23.] doi: 10.3969/j.issn.1002-2104.2015.07.003
[19]	Mo Li, Yu Xinxiao, Zhao Yang, et al.Correlation analysis between urbanization and particle pollution in Beijing. Ecology and Environmental Sciences, 2014, 23(5): 806-811. doi: 10.3969/j.issn.1674-5906.2014.05.012
	[莫莉, 余新晓, 赵阳, 等. 北京市区域城市化程度与颗粒物污染的相关性分析. 生态环境学报, 2014, 23(5): 806-811.] doi: 10.3969/j.issn.1674-5906.2014.05.012
[20]	Pateraki S, Asimakopoulos D N, Flocas H A, et al.The role of meteorology on different sized aerosol fractions (PM10, PM2.5, PM2.5-10). Science of the Total Environment, 2012, 419: 124-135. doi: 10.1016/j.scitotenv.2011.12.064 pmid: 22285081
[21]	Yang Y, Liu X, Qu Y, et al.Formation mechanism of continuous extreme haze episodes in the megacity Beijing, China, in January 2013. Atmospheric Research, 2015, 155: 192-203. doi: 10.1016/j.atmosres.2014.11.023
[22]	Querol X, Alastuey A, Rodriguez S, et al.Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources. Atmospheric Environment, 2001, 35(5): 845-858. doi: 10.1016/S1352-2310(00)00387-3
[23]	Zheng M, Salmon L G, Schauer J J, et al.Seasonal trends in PM2.5 source contributions in Beijing, China. Atmospheric Environment, 2005, 39(22): 3967-3976. doi: 10.1016/j.atmosenv.2005.03.036
[24]	Yangyang Xie, Bin Z, Lin Z, et al.Spatiotemporal variations of PM 2.5 and PM10 concentrations between 31 Chinese cities and their relationships with SO 2, NO2, CO and O3. Particuology, 2015, 20: 141-149. doi: 10.1016/j.partic.2015.01.003
[25]	Wang Zanshan, Li Yunting, Chen Tian, et al.Spatial-temporal characteristics of PM2.5 in Beijing in 2013. Acta Geographica Sinica, 2015, 70(1): 110-120. doi: 10.11821/dlxb201501009
	[王占山, 李云婷, 陈添, 等. 2013年北京市PM2.5的时空分布. 地理学报, 2015, 70(1): 110-120.] doi: 10.11821/dlxb201501009
[26]	Lin Qiaoying, Chen Yongshan.Spatial variation of PM2.5 in cities in winter and anthropogenic influencing factors in China. Journal of Ecology and Rural Environment, 2015, 31(4): 460-465. doi: 10.11934/j.issn.1673-4831.2015.04.003
	[林巧莺, 陈永山. 我国城市冬季PM2.5空间特征及其人为影响因子. 生态与农村环境学报, 2015, 31(4): 460-465.] doi: 10.11934/j.issn.1673-4831.2015.04.003
[27]	Gelencsér A, May B, Simpson D, et al. Source apportionment of PM2.5 organic aerosol over Europe: primary/secondary, natural/anthropogenic,fossil/biogenic origin. Journal of Geophysical Research: Atmospheres, 2007, (1984-2012), 112(D23).
[28]	Briggs D J, Collins S, Elliott P, et al.A Mapping urban air pollution using GIS: A regression-based approach. International Journal of Geographical Information Science, 1997, 11: 699-718. doi: 10.1080/136588197242158
[29]	Wu Jiansheng, Liao Xing, Peng Jian, et al.Simulation and influencing factors of spatial distribution of PM2.5 concentrations in Chongqing. Environmental Science, 2015, 36(3): 759-767. doi: 10.13227/j.hjkx.2015.03.001
	[吴健生, 廖星, 彭建, 等. 重庆市PM2.5浓度空间分异模拟及影响因子. 环境科学, 2015, 36(3): 759-767.] doi: 10.13227/j.hjkx.2015.03.001
[30]	Chen Jingwen, Zou Bin, Wang Min, et al.Bayesian network in PM2.5 concentration prediction considering data characteristics. Environmental Science & Technology, 2015, 38(11): 49-56.
	[陈璟雯, 邹滨, 王敏, 等. 顾及数据特征的贝叶斯网络 PM2.5浓度预报研究. 环境科学与技术, 2015, 38(11): 49-56.]
[31]	Shannon C E.A mathematical theory of communication. ACM SIGMOBILE. Mobile Computing and Communications Review, 2001, 5(1): 3-55.
[32]	Deng Julong.Control problems of grey systems. Systems & Control Letters, 1982, 1(5): 288-294. doi: 10.1016/S0167-6911(82)80025-X
[33]	Liu Sifeng, Dang Yaoguo, Fang Zhigeng, et al.Grey System Theory and Its Application. 5th ed. Beijing: Science Press, 2010: 169-175.
	[刘思峰, 党耀国, 方志耕, 等. 灰色系统理论及其应用. 5版. 北京: 科学出版社, 2010: 169-175.]
[34]	Brus D J, Heuvelink G B.Optimization of sample patterns for universal kriging of environmental variables. Geoderma, 2007, 138(1): 86-95. doi: 10.1016/j.geoderma.2006.10.016

指标层		指标因子	指标层		因素层	指标层		指标因子
空气质量指标与气象要素	大气污染物	PM₁₀(μg/m³) (X₁)	PM_2.5污染来源	交通	等级公路里程(km) (X₁₁)	城市化与产业结构	城市与绿化面积	园林绿地面积*(hm²) (X₂₀)
		SO₂(μg/m³）（X₂）			公路客运量(万人)(X₁₂)			建成区绿地覆盖率*(%)(X₂₁)
		CO (mg/m³)(X₃)			公路货运总量(10 kt) (X₁₃)			人均公园绿地面积* (m²/人) (X₂₂)
		NO₂(μg/m³) (X₄)			机动车保有量(万辆)(X₁₄)			建成区面积(km²)(X₂₃)
		O₃* (μg/m³) （X₅）		工业生产	工业用电量(亿千瓦时)(X₁₅)		人口数量	人口密度(人/km²)(X₂₄)
	气象要素	降水量*(0.1mm) (X₆)		工业废气排放	二氧化硫排放量(t) (X₁₆)		产业结构	第二产业占地区生产总值比重(X₂₅)
		平均风速*(0.1m/s) (X₇)	氮氧化物排放量(t) (X₁₇)			人均地区生产总产值(元) (X₂₆)
		平均气温(0.1℃) (X₈)	烟(粉)尘排放量(t) (X₁₈)			地区工业总产值(亿元)(X₂₇)
		平均相对湿度(1%) (X₉)	建筑		房屋建筑施工面积(10 km²) (X₁₉)	–	–	–
		日照时数 (0.1 h) (X₁₀)		–	–	–	–	–

空气质量指标与气象要素		PM_2.5污染物来源		城市化与产业结构
w_i= 0.2866		w_i= 0.4691		w_i= 0.2453
指标因子	权重(w_i)	指标因子	权重(w_i)	指标因子	权重(w_i)
X₁	0.0889	X₁₁	0.0879	X₂₀	0.0392
X₂	0.0935	X₁₂	0.1190	X₂₁	0.1026
X₃	0.0921	X₁₃	0.0727	X₂₂	0.0924
X₄	0.1124	X₁₄	0.1622	X₂₃	0.2566
X₅	0.0752	X₁₅	0.1225	X₂₄	0.1769
X₆	0.1175	X₁₆	0.0996	X₂₅	0.0534
X₇	0.1269	X₁₇	0.1095	X₂₆	0.1051
X₈	0.1119	X₁₈	0.0970	X₂₇	0.1738
X₉	0.0930	X₁₉	0.1296	–	–
X₁₀	0.0887	–	–	–	–

指标	南京市	无锡市	徐州市	常州市	苏州市	南通市	连云港市	淮安市	盐城市	扬州市	镇江市	泰州市	宿迁市
PM_2.5浓度	73.8167	67.2667	67.2833	66.4833	65.9167	61.5083	61.825	68.65	58.3	65.9333	67.275	72.1667	68.8083
R_{空气质量指标与气象要素}	0.6219	0.6918	0.6244	0.7014	0.6590	0.6993	0.5764	0.6497	0.7390	0.7204	0.6680	0.6285	0.6889
R_{污染物来源}	0.8902	0.7420	0.6613	0.7208	0.5802	0.8317	0.3476	0.5063	0.8409	0.6582	0.5415	0.8490	0.4790
R_{城市化与产业结构}	0.6511	0.6980	0.6815	0.7052	0.7270	0.7358	0.4476	0.5695	0.7118	0.6690	0.6107	0.6865	0.5431

Analysis of the driving factors of PM_2.5 in Jiangsu province based on grey correlation model

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指标因子	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉
关联度(R)	0.7593	0.6334	0.6746	0.6786	0.7435	0.7076	0.6106	0.6506	0.6004
指标因子	X₁₀	X₁₁	X₁₂	X₁₃	X₁₄	X₁₅	X₁₆	X₁₇	X₁₈
关联度(R)	0.6136	0.6645	0.5921	0.7435	0.6640	0.6554	0.6224	0.6363	0.6619
指标因子	X₁₉	X₂₀	X₂₁	X₂₂	X₂₃	X₂₄	X₂₅	X₂₆	X₂₇
关联度(R)	0.5422	0.5546	0.6649	0.6534	0.6750	0.5967	0.6745	0.6543	0.6967

[1]	CHEN Weitong,ZHANG Dong,CUI Dandan,LV Lin,XIE Weijun,SHI Shunjie,HOU Zeyu. Monitoring spatial and temporal changes in the continental coastline and the intertidal zone in Jiangsu province, China [J]. Acta Geographica Sinica, 2018, 73(7): 1365-1380.
[2]	JIN Cheng,XU Jing,HUANG Zhenfang,LU Yuqi,KE Wenqian,CHEN Yu. Regional traffic flow and its influencing factors based on expressway network toll collection data: A case study in Jiangsu province [J]. Acta Geographica Sinica, 2018, 73(2): 248-260.
[3]	Yinan LIN, Xiaobin JIN, Xuhong YANG, Ying LONG, Yinkang ZHOU. Dataset establishment and spatial reconstruction of urban and rural construction land of Jiangsu Province in the past 200 years [J]. Acta Geographica Sinica, 2017, 72(3): 488-506.
[4]	Wenqian KE, Yuqi LU, Wei CHEN, Yihui DING, Qing YANG. Evolutionary stages and theoretical model of high-speed traffic network spatio-temporal structure: A case study of expressway traffic flow network in Jiangsu province [J]. Acta Geographica Sinica, 2016, 71(2): 281-.
[5]	Qian PAN, Xiaobin JIN, Yinkang ZHOU. Gridding reconstruction of land use pattern in Jiangsu Province in the mid-Qing Dynasty [J]. Acta Geographica Sinica, 2015, 70(9): 1449-1462.
[6]	Yuguo TAO, Zhenfang HUANG, Limin WU, Fenglong YU, Kun WANG. Measuring carbon dioxide emissions for regional tourism and its factor decomposition:A case study of Jiangsu province [J]. Acta Geographica Sinica, 2014, 69(10): 1438-1448.
[7]	KE Wenqian, LU Yuqi, YU Zhaoyuan, WANG Han, MA Yingyi. Spatial evolution of economic development at county level in Jiangsu based on multivariate drive [J]. Acta Geographica Sinica, 2013, 68(6): 802-812.
[8]	MA Xiaodong, LI Quanlin, SHEN Yi. Morphological Difference and Regional Types of Rural Settlements in Jiangsu Province [J]. Acta Geographica Sinica, 2012, 67(4): 516-525.
[9]	SUN Wei, CHEN Wen, CHEN Cheng. Study on Cooperative Constraint Regionalization of Water Environment and the Guidance for Industrial Distribution:A Case Study of Jiangsu Province [J]. Acta Geographica Sinica, 2010, 65(7): 819-827.
[10]	JIN Cheng, LU Yuqi. Evolvement of Spatial Pattern of Economy in Jiangsu Province at County Level [J]. Acta Geographica Sinica, 2009, 64(6): 713-724.
[11]	MA Ronghua, GU Chaolin, PU Yingxia, MA Xiaodong, ZHU Chuangeng. Urban Spatial Sprawl Pattern and Metr ics in South of Jiangsu Province along the Yangtze River [J]. Acta Geographica Sinica, 2007, 62(10): 1011-1022.
[12]	ZHU Jie, GUAN Weihua, JIANG Zhixin, ZHEN Feng. The Evolution of Urban Economic Effect Regions in Jiangsu Province since 1978 [J]. Acta Geographica Sinica, 2007, 62(10): 1023-1033.
[13]	PU Yingxia, MA Ronghua, GE Ying, HUANG Xingyuan. Spatial-Temporal Dynamics of Jiangsu Regional Convergence with Spatial Markov Chains Approach [J]. Acta Geographica Sinica, 2005, 60(5): 817-826.
[14]	MA Xiaodong, MA Ronghua, XU Jiangang. Spatial Structure of Cities and Towns with ESDA-GIS Framework [J]. Acta Geographica Sinica, 2004, 59(6): 1048-1057.
[15]	OUXiangjun,GUChaolin. Quantitative Analysis of Regional Economic Polarization and Dynamical Mechanisms in Jiangsu Province [J]. Acta Geographica Sinica, 2004, 59(5): 791-799.

Analysis of the driving factors of PM2.5 in Jiangsu province based on grey correlation model

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Analysis of the driving factors of PM_2.5 in Jiangsu province based on grey correlation model