长江流域资源与环境 >> 2015, Vol. 24 >> Issue (09): 1458-1463.doi: 10.11870/cjlyzyyhj201509003

• 区域可持续发展 • 上一篇    下一篇

城市土地利用类型与PM2.5浓度相关性研究——以武汉市为例

唐昀凯, 刘胜华   

  1. 武汉大学资源与环境科学学院, 湖北 武汉 430079
  • 收稿日期:2014-11-20 修回日期:2015-03-23 出版日期:2015-09-20
  • 作者简介:唐昀凯(1990~),男,硕士研究生,主要研究方向为土地利用规划.E-mail:864092531@qq.com
  • 基金资助:
    国家自然科学基金项目(41171312)资助

Research on the correlation between urban land use types and PM2.5 concentrations in wuhan

TANG Yun-kai, LIU Sheng-hua   

  1. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
  • Received:2014-11-20 Revised:2015-03-23 Online:2015-09-20

摘要: 随着我国工业化的不断发展,在我国的主要经济发展地区的雾霾天气不断爆发,使我国的大气环境日益恶化,严重影响了人们的日常生活和身体健康。PM2.5作为雾霾的重要组成成分,也日渐成为环境领域的研究热点问题。随着全球性变化研究领域逐渐加强了对土地利用与生态环境的相关研究,因此无论从法律和社会经济发展的角度,还是从生态资源保护与环境可持续发展的角度,土地利用与PM2.5的相关研究都显得相当重要。研究目的:分析武汉市各类用地类型与PM2.5浓度的相关性程度。研究方法:使用ENVI与ArcGIS对武汉市2013年MODIS气溶胶产品进行空间分析与插值处理,再应用SPSS将其与武汉市2013年10个观测点的PM2.5浓度数据作相关性分析,以证实MODIS气溶胶厚度与PM2.5浓度的相关性,并建立两者的线性回归方程,然后利用计算后的武汉市整体PM2.5浓度分布与各土地利用类型进行相关性研究。研究结果:武汉市PM2.5浓度有明显的空间分布特征,绿化面积比例与PM2.5浓度呈显著负相关,建设用地面积比例与PM2.5浓度呈显著正相关,未利用地面积比例虽然与PM2.5浓度呈正相关,但相关性较低,而耕地与水体对PM2.5浓度没有显著影响。研究结论:土地利用类型对武汉市PM2.5浓度的分布有显著的影响,其与搭载MODIS传感器的遥感卫星监测方式的结合能成为研究大范围特定区域PM2.5浓度空间格局的新方法,并且增加城市绿化面积,控制建设用地规模能有效减少PM2.5浓度。

关键词: 土地生态, MODIS, PM2.5浓度, 空间相关性, 气溶胶光学厚度, 土地利用类型

Abstract: With the continuous development of China's industrialization, haze weather broke out continuously at the main economic development area of China, which seriously affects people's daily life and health. PM2.5, which is known as an important component of haze, becomes a hotspot issue in the field of environment science. A large number of studies shows that PM2.5 can absorb toxic substances in the air, and through the respiratory tract into the body, which cause the allergic patients suffering from respiratory or cardiovascular disease at risk. Also, PM2.5 is closely related with the recently reduction of city atmospheric visibility, which can lead to traffic accident. In addition, with the global change research field gradually strengthening the related research of land use and ecological environment, the related research of land use and PM2.5 has become very important from the social economic development point of view, and from the environment sustainable development perspective. The purpose of this paper is to analyze the correlation of various land use types and concentrations of PM2.5 in Wuhan City. We used the ArcGIS and ENVI software to perform spatial analysis and interpolation based on Wuhan's 2013 MODIS aerosol products, then we used SPSS to analyze PM2.5 concentration data from 10 stations in Wuhan to confirm the MODIS aerosol optical thickness relevance to PM2.5 concentrations, and established the linear regression model to study the correlation between the calculated overall PM2.5 concentrations in Wuhan city and various land use types. Results showed that PM2.5 concentrations had obvious spatial distribution characteristics in Wuhan City, green land and PM2.5 concentrations had significantly negative correlation, the correlation between construction land and PM2.5 concentrations was significantly positive, and arable land had no significant effects on PM2.5 concentration. Through the comparison among the absolute value of these five land-use type, we can find out that the rank of water and arable changing at the three days, which shows there may be other factors such as soil composition, water composition, spectral radiation influencing PM2.5 concentrations. Our conclusions are that land-use has a significant effect on distribution of PM2.5 concentration in Wuhan City, and remote sensing satellite monitoring methods based on MODIS sensor is a new method of researching space distribution of PM2.5 concentration at a particular area. In addition, increasing the urban greening area or controlling the ratio of construction land can effectively reduce PM2.5 concentrations.

Key words: Land ecology, MODIS, PM2.5 concentrations, spatial correlation, aerosol optical thickness, land use type

中图分类号: 

  • P951
[1] POPE C A III, BURNETT R T, THUN M J, et al. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution[J]. JAMA, 2002, 287(9): 1132-1141.
[2] PETERS A, DOCKERY D W, MULLER J E, et al. Increased particulate air pollution and the triggering of myocardial infarction[J]. Circulation, 2001,103(23): 2810-2815.
[3] HOEK G, MELIEFSTE K, CYRYS J, et al. Spatial variability of fine particle concentrations in three European areas[J]. Atmospheric Environment, 36(25): 4077-4088.
[4] MARTUZEVICIUS D, GRINSHPUN S A, REPONEN T, et al. Spatial and temporal variations of PM2.5 concentration and composition throughout an urban area with high freeway density-the Greater Cincinnati study[J]. Atmospheric Environment, 2004, 38(8): 1091-1105.
[6] 袁杨森,刘大锰,车瑞俊,等.北京市秋季大气颗粒物的污染特征研究[J].生态环境, 2007,16(1):18-25.
[7] ICHOKU C, CHU D A, MATTOO S, et al. A spatio-temporal approach for global validation and analysis of MODIS aerosol products[J]. Geophysical Research Letters, 2002, 29(12): MOD1-1-MOD1-4.
[8] LEVY R C, REMER L A, KLEIDMAN R G, et al. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land[J]. Atmospheric Chemistry and Phys,2010(10):14815-14873.
[1] 朱婧瑄, 齐述华, 刘贵花, 王点, 熊梦雅. 2000~2013年鄱阳湖流域蒸散量时空变化[J]. 长江流域资源与环境, 2016, 25(Z1): 9-16.
[2] 彭焕华, 李朝奎, 唐志光, 梁继. 丹江口库区陆地植被物候空间格局及其与海拔的响应关系[J]. 长江流域资源与环境, 2016, 25(10): 1626-1634.
[3] 张煦, 马驿, 郑雯, 汪善勤. 基于时序MODIS-NDVI的油菜种植面积变化趋势分析——以江汉平原为例[J]. 长江流域资源与环境, 2016, 25(03): 412-419.
[4] 陈颖锋, 王玉宽, 傅斌, 刘勤, 王跚. 基于MODIS地面温度数据的成都市热岛时空变化[J]. 长江流域资源与环境, 2016, 25(01): 156-162.
[5] 张延兵, 陶建斌, 刘鹏程, 徐京京. 基于时间序列MODIS数据的贵州省森林物候地域分异[J]. 长江流域资源与环境, 2015, 24(11): 1891-1897.
[6] 朱希扬, 潘晨, 刘敏, 杨芳, 贾文晓, 象伟宁. 上海春季近地面大气CO2浓度空间分布特征及其影响因素分析[J]. 长江流域资源与环境, 2015, 24(09): 1443-1450.
[7] 刘振波, 张明明, 葛云健, 邱斌. 基于MODIS AOD数据的南京市大气能见度估算[J]. 长江流域资源与环境, 2015, 24(09): 1451-1457.
[8] 吕剑成, 周磊, 洪武扬, 李满春, 黄秋昊. 城市土地生态适宜性分区划分研究[J]. 长江流域资源与环境, 2015, 24(09): 1560-1567.
[9] 易予晴, 龙腾飞, 焦伟利, 彭剑威, 刘慧婵, 凌赛广. 武汉城市群夏季热岛特征及演变[J]. 长江流域资源与环境, 2015, 24(08): 1279-1285.
[10] 赵丽红, 陈文波, 邵虹. 南昌市中心城区城市建设用地集约利用特征及空间相关性分析[J]. 长江流域资源与环境, 2015, 24(08): 1286-1292.
[11] 梁文广,赵英时,周霞,. 基于MODIS数据的地表组分温度反演研究[J]. 长江流域资源与环境, 2008, 17(6): 948-948.
[12] 李俊杰,何隆华,陈 杰. 南京城市热场的卫星遥感分析[J]. 长江流域资源与环境, 2005, 14(6): 760-763.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 陈亚华,黄少华,刘胜环,王桂萍,丁 锋,邵志成,沈振国. 南京地区农田土壤和蔬菜重金属污染状况研究[J]. 长江流域资源与环境, 2006, 15(3): 356 -360 .
[2] 王 初, 陈振楼, 王 京, 周乃晟, 许世远. 上海市崇明岛公路两侧土壤重金属污染研究[J]. 长江流域资源与环境, 2008, 17(1): 105 .
[3] 班军梅,缪启龙,李 雄. 西南地区近50年来气温变化特征研究[J]. 长江流域资源与环境, 2006, 15(3): 346 -351 .
[4] 马鹏红,黄贤金,于术桐,邬 震. 江西省上饶县农户水土保持投资行为机理与实证模型[J]. 长江流域资源与环境, 2004, 13(6): 568 -572 .
[5] 姚海林,杨 洋, 谷志孟. 垃圾块体填埋法及其应用前景[J]. 长江流域资源与环境, 2005, 14(1): 123 -126 .
[6] 雷广海|刘友兆,陆效平. 江苏省13城市土地利用集约度时空变异及驱动因素[J]. 长江流域资源与环境, 2009, 18(1): 7 .
[7] 娄保锋,陈永柏,翁立达, 臧小平,束金祥,宋江英,刘 成. 水样不同处理方式对高锰酸盐指数测定值的影响[J]. 长江流域资源与环境, 2008, 17(1): 143 .
[8] 李成芳, 曹凑贵, 汪金平, 展茗, 蔡明历. 稻鸭、稻鱼共作对稻田P素动态变化的影响[J]. 长江流域资源与环境, 2009, 18(2): 126 .
[9] 戴天晟 孙绍荣 赵文会 顾宝炎. 区域水资源可持续利用评价的FAHP-PP模型[J]. 长江流域资源与环境, 2009, 18(5): 421 .
[10] 周旭, 张斌, 刘刚才. 元谋干热河谷近30年植被变化遥感监测[J]. 长江流域资源与环境, 2010, 19(11): 1309 .