大型垃圾处理设施周边居民污染感知影响因素及空间特征

doi:10.18306/dlkxjz.2021.06.008

Abstract

Abstract:

In recent years, with the rapid development of large cities in China, large-scale waste treatment facilities and residential spaces overlap are affecting residents' quality of life. Taking Beijing's largest waste treatment center—Asuwei as the research object, this study built a multilayer linear model based on data from a questionnaire survey conducted in August to October 2019 to examine the relationship between residents' perception of pollution and distance from the source of pollution, residents' individual attributes, and residents' subjective perceptions. Using this model, we explored the influencing factors of residents' perception of pollution around neighboring facilities. On this basis, combined with demographic characteristics of the areas around waste treatment facility, the spatial characteristics of residents' pollution perception were analyzed. The study found that: 1) In terms of individual attributes, factors such as education background and household income have a positive effect on residents' perception of pollution; "old Beijingers" are most sensitive to pollution from waste disposal centers, while "new Beijingers" are the least sensitive; residents aged 30-40 have the strongest perception of pollution. 2) In terms of subjective perception, residents who are aware of the surrounding pollution sources and believe that they are exposed to great pollution hazards are more sensitive to pollution. 3) Distance to Asuwei directly affects residents' perception of pollution, as well as the degree to which other factors affect residents' perception of pollution. The farther the distance, the greater the impact of education, household income, and knowledge of pollution sources on pollution perception. 4) The residential communities within 9 km from Asuwei gather a large number of low-income and less educated people. Such residents lack the necessary protection awareness and need to be paid attention to. There are more highly educated "new Beijingers" in the Huilongguan-Tiantongyuan area that is 9-12 km away from Asuwei, and in this area residents' perception of pollution varies greatly. Residents in the areas more than 12 km away from Asuwei have weak perception of pollution, and the negative perception of these residents can be eliminated through positive guidance.

Key words: pollution perception, multilayer linear model, influencing factors, Beijing

DANG Yi, TANG Qing, YU Jianhui, ZHANG Wenzhong, LI Jiaming. Influencing factors and spatial characteristics of residents’ pollution perception around large-scale waste treatment facilities[J].PROGRESS IN GEOGRAPHY, 2021, 40(6): 980-990.

Figures/Tables 8

Fig.1

Tab.1

Tab.2

Fig.2

Tab.3

Tab.4

Tab.5

Fig.3

References 36

[1]	彭建, 周尚意. 公众环境感知与建立环境意识: 以北京市南沙河环境感知调查为例[J]. 人文地理, 2001,16(3):21-25.
	[ Peng Jian, Zhou Shangyi. Environmental perception and awareness building of Beijing citizens: A case study of Nansha River. Human Geography, 2001,16(3):21-25. ]
[2]	Engel U, Pötschke M. Willingness to pay for the environment: Social structure, value orientations and environmental behaviour in a multilevel perspective[J]. Innovation: The European Journal of Social Science Research, 1998,11(3):315-332. doi: 10.1080/13511610.1998.9968571
[3]	Pantavou K, Lykoudis S, Psiloglou B. Air quality perception of pedestrians in an urban outdoor Mediterranean environment: A field survey approach[J]. Science of the Total Environment, 2017,574:663-670. doi: 10.1016/j.scitotenv.2016.09.090
[4]	Nikolopoulou M, Kleissl J, Linden P F, et al. Pedestrians' perception of environmental stimuli through field surveys: Focus on particulate pollution[J]. Science of the Total Environment, 2011,409(13):2493-2502. doi: 10.1016/j.scitotenv.2011.02.002
[5]	Kim M, Yi O, Kim H. The role of differences in individual and community attributes in perceived air quality[J]. Science of the Total Environment, 2012,425:20-26. doi: 10.1016/j.scitotenv.2012.03.016
[6]	Rotko T, Oglesby L, Künzli N, et al. Determinants of perceived air pollution annoyance and association between annoyance scores and air pollution (PM_2.5, NO₂) concentrations in the European EXPOLIS study[J]. Atmospheric Environment, 2002,36(29):4593-4602. doi: 10.1016/S1352-2310(02)00465-X
[7]	Williams I D, Bird A. Public perceptions of air quality and quality of life in urban and suburban areas of London[J]. Journal of Environmental Monitoring, 2003,5(2):253-259. doi: 10.1039/b209473h
[8]	Atari D O, Luginaah I N, Fung K. The relationship between odour annoyance scores and modelled ambient air pollution in Sarnia, "Chemical Valley", Ontario[J]. International Journal of Environmental Research and Public Health, 2009,6(10):2655-2675. doi: 10.3390/ijerph6102655
[9]	Kohlhuber M, Mielck A, Weiland S K, et al. Social inequality in perceived environmental exposures in relation to housing conditions in Germany[J]. Environmental Research, 2006,101(2):246-255. doi: 10.1016/j.envres.2005.09.008
[10]	史兴民, 刘戎. 煤矿区居民的环境污染感知: 以陕西省韩城矿区为例[J]. 地理研究, 2012,31(4):641-651.
	[ Shi Xingmin, Liu Rong. Perception of environmental pollution in a coal mine area: A case study of Hancheng Mine Area, Shaanxi Province. Geographical Research, 2012,31(4):641-651. ]
[11]	王玉君, 韩冬临. 空气质量、环境污染感知与地方政府环境治理评价[J]. 中国软科学, 2019(8):41-51.
	[ Wang Yujun, Han Donglin. Air quality, perception of environmental pollution, and the evaluation of local governments' environmental governance. China Soft Science, 2019(8):41-51. ]
[12]	Klæboe R, Kolbenstvedt M, Clench-Aas J, et al. Oslo traffic study, part 1: An integrated approach to assess the combined effects of noise and air pollution on annoyance[J]. Atmospheric Environment, 2000,34:4727-4736. doi: 10.1016/S1352-2310(00)00304-6
[13]	Flynn J, Slovic P, Mertz C K. Gender, race, and perception of environmental health risks[J]. Risk Analysis, 1994,14(6):1101-1108. pmid: 7846319
[14]	Guo Y, Li Y W. Online amplification of air pollution risk perception: The moderating role of affect in information[J]. Information, Communication & Society, 2018,21(1):80-93.
[15]	Bush J, Moffatt S, Dunn C E. Keeping the public informed? Public negotiation of air quality information[J]. Public Understanding of Science, 2001,10:213-229. doi: 10.1088/0963-6625/10/2/304
[16]	谭爽, 胡象明. 邻避型社会稳定风险中风险认知的预测作用及其调控: 以核电站为例[J]. 武汉大学学报(哲学社会科学版), 2013,66(5):75-81.
	[ Tan Shuang, Hu Xiangming. The predictive role and regulation of risk perception in social risk of NIMBY project: Taking nuclear power plant as an example. Wuhan University Journal (Philosophy & Social Sciences), 2013,66(5):75-81. ]
[17]	Bickerstaff K, Walker G. Public understandings of air pollution: The 'localisation' of environmental risk[J]. Global Environmental Change, 2001,11(2):133-145. doi: 10.1016/S0959-3780(00)00063-7
[18]	史兴民, 刘春霞. 煤矿区居民对环境问题的感知: 以陕西省彬长矿区为例[J]. 干旱区地理, 2012,35(4):631-638.
	[ Shi Xingmin, Liu Chunxia. Perception difference of environmental problem in coal mine area: A case of Binchang Mine Area, Shaanxi Province. Arid Land Geography, 2012,35(4):631-638. ]
[19]	Lima M L. On the influence of risk perception on mental health: Living near an incinerator[J]. Journal of Environmental Psychology, 2004,24(1):71-84. doi: 10.1016/S0272-4944(03)00026-4
[20]	Ma J, Mitchell G, Dong G P, et al. Inequality in Beijing: A spatial multilevel analysis of perceived environmental hazard and self-rated health[J]. Annals of the American Association of Geographers, 2017,107(1):109-129. doi: 10.1080/24694452.2016.1224636
[21]	崔晶, 亓靖. 邻避事件中地方政府行为选择探析: 以北京阿苏卫邻避抗争事件为例[J]. 南京工业大学学报(社会科学版), 2017,16(4):28-39.
	[ Cui Jing, Qi Jing. Analysis of local government behavior choice in NIMBY incident: Taking Beijing Asuwei NIMBY protest as an example. Journal of Nanjing Tech University (Social Science Edition), 2017,16(4):28-39. ]
[22]	党艺, 余建辉, 张文忠. 环境类邻避设施对北京市住宅价格影响研究: 以大型垃圾处理设施为例[J]. 地理研究, 2020,39(8):1769-1781. doi: 10.11821/dlyj020190689
	[ Dang Yi, Yu Jianhui, Zhang Wenzhong. The impact of NIMBY facilities on housing prices in Beijing: A case study of large waste disposal facilities. Geographical Research, 2020,39(8):1769-1781. ]
[23]	Dragano N, Hoffmann B, Moebus S, et al. Traffic exposure and subclinical cardiovascular disease: Is the association modified by socioeconomic characteristics of individuals and neighbourhoods? Results from a multilevel study in an urban region[J]. Occupational & Environmental Medicine, 2009,66(9):628-635.
[24]	党云晓, 余建辉, 张文忠, 等. 北京居民生活满意度的多层级定序因变量模型分析[J]. 地理科学, 2016,36(6):829-836. doi: 10.13249/j.cnki.sgs.2016.06.004
	[ Dang Yunxiao, Yu Jianhui, Zhang Wenzhong, et al. Influencing factors of residents' life satisfaction: A study based on ordered category response multilevel modelling in Beijing. Scientia Geographica Sinica, 2016,36(6):829-836. ]
[25]	Perlin S A, Wong D, Sexton K. Residential proximity to industrial sources of air pollution: Interrelationships among race, poverty, and age[J]. Journal of the Air & Waste Management Association, 2001,51(3):406-421.
[26]	Goldstein H. Multilevel Statistical Methods[M]. London, UK: Edward Arnold, 1995.
[27]	刘红云, 孟庆茂. 教育和心理研究中的多层线性模型[J]. 心理科学进展, 2002,10(2):213-219.
	[ Liu Hong-yun, Meng Qingmao. Hierarchical linear model in the study of education and psychology. Advances in Psychological Science, 2002,10(2):213-219. ]
[28]	杨卡. 大北京人口分布格局与多中心性测度[J]. 中国人口·资源与环境, 2015,25(2):83-89.
	[ Yang Ka. Population distribution and multicenter measurement of great Beijing. China Population, Resources and Environment, 2015,25(2):83-89. ]
[29]	穆晓燕, 王扬. 大城市社会空间演化中的同质聚居与社区重构: 对北京三个巨型社区的实证研究[J]. 人文地理, 2013,28(5):24-30.
	[ Mu Xiaoyan, Wang Yang. Homogeneity of habitation and the community reconstruction in urban spatial evolvement in big cities: A case study of three mega-communities in Beijing. Human Geography, 2013,28(5):24-30. ]
[30]	柴宏博, 冯健. 基于迁居的郊区大型居住区社会空间形成: 以北京回龙观居住区为例[J]. 地域研究与开发, 2014,33(5):77-81.
	[ Chai Hongbo, Feng Jian. Formation of social space in suburban large communities based on residents' intra-urban migration: A case study of Huilongguan in Beijing. Areal Research and Development, 2014,33(5):77-81. ]
[31]	Jacquemin B, Sunyer J, Forsberg B, et al. Annoyance due to air pollution in Europe[J]. International Journal of Epidemiology, 2007,36(4):809-820. doi: 10.1093/ije/dym042
[32]	Kohlhuber M, Mielck A, Weiland S K, et al. Social inequality in perceived environmental exposures in relation to housing conditions in Germany[J]. Environmental Research, 2006,101(2):246-255. doi: 10.1016/j.envres.2005.09.008
[33]	郭文伯, 张艳, 柴彦威. 城市居民出行的空气污染暴露测度及其影响机制: 北京市郊区社区的案例分析[J]. 地理研究, 2015,34(7):1310-1318.
	[ Guo Wenbo, Zhang Yan, Chai Yanwei. Measurement of residents' daily travel air pollution exposure and its impact mechanism: A case study of suburban communities in Beijing. Geographical Research, 2015,34(7):1310-1318. ]
[34]	马静, 柴彦威, 符婷婷. 居民时空行为与环境污染暴露对健康影响的研究进展[J]. 地理科学进展, 2017,36(10):1260-1269. doi: 10.18306/dlkxjz.2017.10.008
	[ Ma Jing, Chai Yanwei, Fu Tingting. Progress of research on the health impact of people's space-time behavior and environmental pollution exposure to health effects. Progress in Geography, 2017,36(10):1260-1269. ]
[35]	Stevenson M, Thompson J, de Sá T H, et al. Land use, transport, and population health: Estimating the health benefits of compact cities[J]. The Lancet, 2016,388:2925-2935. doi: 10.1016/S0140-6736(16)30067-8
[36]	Giles-Corti B, Vernez-Moudon A, Reis R, et al. City planning and population health: A global challenge[J]. The Lancet, 2016,388:2912-2924. doi: 10.1016/S0140-6736(16)30066-6

组内相关程度	ICC1		ICC2
组内相关程度	ICC1范围	适宜分层程度	ICC2范围	适宜分层程度
低度相关	ICC1≤0.059	不适宜	ICC2≤0.75	不适宜
中度相关	0.059<ICC1≤0.138	较适宜
高度相关	ICC1>0.138	十分适宜	ICC2>0.75	适宜

街道/乡镇名称	距离/m	本地人口占比排名	人口数量排名	人口密度排名	街道学历占比排名
街道/乡镇名称	距离/m	本地人口占比排名	人口数量排名	人口密度排名	高中及以下	大学本科及以上
百善镇	1539	172	248	209	140	171
小汤山镇	5334	223	128	199	137	186
沙河地区	5456	284	52	162	219	168
北七家镇	7704	321	3	138	153	154
南邵镇	8182	175	234	208	125	201
回龙观地区	9051	317	2	8	312	19
东小口地区	10077	322	1	50	274	61
城北街道	12088	216	10	104	276	40
西三旗街道	12650	260	25	63	303	27
东升地区	13191	305	147	122	27	5
城南街道	13251	303	118	140	216	161
清河街道	13412	269	32	77	257	60
西北旺镇	14215	312	27	152	243	244
上地街道	14360	309	66	100	320	265
马连洼街道	14986	270	59	103	289	39

变量类型	变量	取值分布(比例)
距离(社区层级)	到阿苏卫垃圾处理中心的距离	范围：2~15 km,平均值：8.69 km
距离(社区层级)	到六里屯垃圾填埋场距离	范围：0.4~20 km,平均值：12.49 km
方位(社区层级)	位于阿苏卫垃圾处理中心的方位	东南(51.22%);西南(31.71%);东北(7.31%);西北(9.76%)
个人属性 (个体层级)	学历	初中及以下(15.22%);高中(21.22%);大专(24.11%);大学(32.76);研究生及以上(6.69%)
	家庭年收入	0~5万元(15.8%);5万~7万元(12%);7万~9万元(11.19%);10万~20万元(28.03%);20万~30万元(19.72%);30万~50万元(9.34%);大于50万元(3.92%)
	家庭汽车数量	范围：0~4辆;均值：0.67辆
	迁居计划	有(16.03%);无(83.97%)
	年龄阶段	30岁以下(40.37%);30~40岁(26.65%);40~50岁(14.76%);50岁以上(18.22%)
	户籍	籍贯户籍均北京(29.99%);外地籍贯北京户口(4.27%);外地籍贯外地户口(65.74%)
主观认知 (个体层级)	购房前是否知道周边污染源	知道(10.27%);不知道(89.73%)
	是否知道阿苏卫垃圾处理中心	知道(38.18%);不知道(61.82%)
	是否知道六里屯垃圾填埋场	知道(13.26%);不知道(86.74%)
	污染要素对房价的影响程度	范围：0~10;平均值：5.7;标准差：3.44
	认为大型垃圾处理设施是对房价影响最大的污染要素	是(35.87%);否(64.13%)
	小区垃圾堆废物污染程度	范围：0~10;平均值：1.55;中位数：2.66

变量类型	变量	回归系数
距离(社区层级)	到六里屯垃圾填埋场距离	0.238^**
距离(社区层级)	到阿苏卫垃圾处理中心的距离	0.289^***
方位(社区层级)	阿苏卫垃圾处理中心的方位(对照组：位于西南)
	位于东南	1.975^*
	位于西北	1.943^**
	位于东北	3.373^***
个人属性 (个体层级)	学历(共5级,从低到高为1~5)	0.193^*
	家庭收入(共7级,从低到高为1~7)	0.089^*
	家庭汽车数量	-0.450^***
	3~5 a内是否有迁居计划(对照组：无迁居计划)
	有迁居计划	-0.713^**
	年龄阶段(对照组：30岁以下)
	30~40岁	0.542^**
	40~50岁	0.375^*
	50岁以上	0.435^*
	户籍(对照组：籍贯户口均北京)
	籍贯外地有北京户口	-1.420^***
	籍贯外地无北京户口	-0.518^***
主观认知 (个体层级)	购房前是否知道周边污染源(对照组：不知道)
	知道	1.513^***
	当前是否知道阿苏卫垃圾处理中心(对照组：不知道)
	知道	1.664^***
	当前是否知道六里屯垃圾填埋场(对照组：不知道)
	知道	0.342
	认为污染要素对房价的影响程度(共10级,从低到高为1~10)	0.082^***
	大型垃圾处理设施是对房价影响最大的污染要素(对照组：不认为)
	认为	1.667^**
	小区垃圾堆废物污染程度(共10级,从低到高为1~10)	0.109^**

调整变量	回归系数
到阿苏卫距离与学历	0.071^***
到阿苏卫距离与家庭收入	0.043^***
到阿苏卫距离与迁居计划	0.049
到阿苏卫距离与购房前是否知道周边污染源	0.237^*

Influencing factors and spatial characteristics of residents’ pollution perception around large-scale waste treatment facilities

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 36

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LIU Shizhe, XIE Miaomiao, WU Rongrong, WANG Yanan, LI Xinyu. Influence of the choice of geographic unit on the response of urban thermal environment:Taking Beijing as an example [J]. PROGRESS IN GEOGRAPHY, 2021, 40(6): 1037-1047.
[2]	JIANG Haibing, LI Yejin. Change of spatial structure of manufacturing industry in the Beijing-Tianjin-Hebei region and its driving factors [J]. PROGRESS IN GEOGRAPHY, 2021, 40(5): 721-735.
[3]	ZHAO Pengjun, LUO Jia, HU Haoyu. Spatial match between residents’ daily life circle and public service facilities using big data analytics: A case of Beijing [J]. PROGRESS IN GEOGRAPHY, 2021, 40(4): 541-553.
[4]	LI Yanxi, CHAI Yanwei, ZHANG Yan. Weekly spatiotemporal behavior of suburban residents in family context: A case study of the Shangdi-Qinghe area in Beijing [J]. PROGRESS IN GEOGRAPHY, 2021, 40(4): 597-606.
[5]	XU Jingxue, CHEN Li, ZHANG Wenzhong. Does the built environment impact residents’ healthy lifestyle? Empirical analysis based on 22 communities in Beijing [J]. PROGRESS IN GEOGRAPHY, 2021, 40(4): 660-670.
[6]	JIANG Wanbei, LIU Weidong, LIU Zhigao, HAN Mengyao. Inequality and driving forces of energy-related CO₂ emissions intensity in China [J]. PROGRESS IN GEOGRAPHY, 2020, 39(9): 1425-1435.
[7]	FU Zhanhui, MEI Lin, ZHENG Rumin, WANG Tongtong. Spatial differentiation mechanism of urban female employment rate in Northeast China [J]. PROGRESS IN GEOGRAPHY, 2020, 39(8): 1308-1318.
[8]	CHENG Hanbei, ZOU You, LIN Sainan, LI Zhigang. Review of recent research on the impacts of residential mobility on health [J]. PROGRESS IN GEOGRAPHY, 2020, 39(7): 1210-1223.
[9]	DING Jianjun, WANG Zhang, LIU Yanhong, YU Fangwei. Measurement of economic resilience of contiguous poverty-stricken areas in China and influencing factor analysis [J]. PROGRESS IN GEOGRAPHY, 2020, 39(6): 924-937.
[10]	ZHU Guanyu, CHEN Peng. Spatial evolution and influencing factors of criminals’ cross-area co-offending network: An example of fraud in Beijing [J]. PROGRESS IN GEOGRAPHY, 2020, 39(5): 792-803.
[11]	GUO Yaqi, CHEN Peng. Formation and feature analyses of crime hotspots using near repeat principle: A case study of robbery in Beijing [J]. PROGRESS IN GEOGRAPHY, 2020, 39(5): 804-814.
[12]	ZHANG Feng, CHEN Yanguang, LIU Peng. Spatiotemporal relationships between urban system and water system in the Beijing-Tianjin-Hebei region [J]. PROGRESS IN GEOGRAPHY, 2020, 39(3): 377-388.
[13]	LU Minghua, ZHOU Yueyan, LIU Hanchu, XU Xin. Spatiotemporal dynamics and influencing factors of direct investment in Hebei Province by Beijing’s enterprises [J]. PROGRESS IN GEOGRAPHY, 2020, 39(3): 389-401.
[14]	LUO Kui, LI Guangdong, LAO Xin. Spatial reconstruction and optimization of industrial development in the Beijing-Tianjin-Hebei urban agglomeration [J]. PROGRESS IN GEOGRAPHY, 2020, 39(2): 179-194.
[15]	LI Xiaoli, SONG Weixuan, WU Wei, MA Yuzhu. Beijing urban spatial cognition based on follow-up investigations:Take graduate students in the University of Chinese Academy of Sciences as an example [J]. PROGRESS IN GEOGRAPHY, 2020, 39(2): 276-285.