PM<sub>2.5</sub>与土地利用类型关系的四季差异分析:以潍坊市为例

张阔; 张永彬; 李成名; 戴昭鑫

doi:10.13205/j.hjgc.202104012

PM_2.5与土地利用类型关系的四季差异分析:以潍坊市为例

doi: 10.13205/j.hjgc.202104012

张阔^1,2,
张永彬^1, ,,
李成名²,
戴昭鑫²

1. 华北理工大学矿业工程学院, 河北唐山 063210;
2. 中国测绘科学研究院, 北京 100830

基金项目:

国家重点研发计划（2018YFD1100300）；国家自然科学基金项目（41907389）；中国测绘科学研究院基本科研业务费（AR2010）。

详细信息

作者简介:
张阔(1995-),男,硕士,主要从事3S技术在资源与环境中的应用。zk934729803@163.com

通讯作者:
张永彬(1969-),男,博士,教授,主要研究方向为3S技术在资源与环境中的应用,地理国情监测。zyb063009@yeah.net

计量
- 文章访问数: 235
- HTML全文浏览量: 22
- PDF下载量: 12
- 被引次数: 0
出版历程
- 收稿日期: 2020-05-27
- 网络出版日期: 2021-07-21

SEASONAL DIFFERENCE ANALYSIS OF THE RELATIONSHIP BETWEEN PM_2.5 AND LAND USE: A CASE STUDY OF WEIFANG

1. School of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China;
2. China Academy of Surveying and Mapping, Beijing 100830, China

摘要

摘要: 土地利用类型可显著影响PM_2.5的污染浓度，研究两者间的关系，对于大气污染治理具有重要意义。目前现有研究大多基于回归模型分析土地利用类型，在年尺度上对PM_2.5的固定影响，而土地利用类型与PM_2.5之间关系复杂，且在不同季节影响力度也不尽相同。基于此，利用增强回归树定量化探讨了不同季节土地利用类型对PM_2.5浓度影响的贡献率，结果表明：土地利用类型在不同季节中对PM_2.5浓度影响存在显著差异，春、夏、秋、冬四季的主导因素分别为支路（27.37%）、次干路（19.17%）、植被覆盖面（37.23%）、建设用地（86.37%）。此外，根据不同季节各土地利用类型对PM_2.5的影响力变化曲线，提供了在优化绿化建设布局和加强交通管控力度两方面的量化指标，可为潍坊市PM_2.5污染治理提供规划参考。
- 土地利用 /
- 增强回归树模型 /
- PM_2.5 /
- 贡献率 /
- 季节
Abstract: With the acceleration of industrialization and urbanization, China's frequent occurrence of haze weather has seriously affected People's daily life and health. As an important part of haze, PM_2.5 has become a hot issue. The type of land use can significantly affect the pollution concentration of PM_2.5, and the study of the relationship between the type of land use and PM_2.5 pollution is of great significance for air pollution control. At present, most of the existing researches are based on regression model to analyze the fixed influence of land use type on PM_2.5 on an annual scale. However, the relationship between land use type and PM_2.5 is complex, and the influence intensity is not the same in different seasons. Based on this, the contribution rate of land use type to PM_2.5 concentration in different seasons was quantitatively discussed by using the method of boosted regression tree. The results showed that the land use type had significant difference in the influence of PM_2.5 concentration in different seasons, and the dominant factors in spring, summer, autumn and winter were branch road (27.37%), secondary trunk road (19.17%), vegetation coverage (37.23%) and construction land (86.37%). In addition, according to the influence curve of different land use types on PM_2.5 in different seasons, specific and quantitative planning indicators of optimizing the layout of greening construction and strengthening traffic control were provided for the control of PM_2.5 pollution in Weifang.
- land use /
- boosted regression tree model /
- PM_2.5 /
- contribution rate /
- season

HTML全文

参考文献(25)

[1]	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.
[2]	POPE III C A, 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.
[3]	张润森, 濮励杰, 刘振. 土地利用/覆被变化的大气环境效应研究进展[J]. 地域研究与开发, 2013,32(4):123-128.
[4]	阳海鸥, 陈文波, 梁照凤. LUR模型模拟的南昌市PM_2.5浓度与土地利用类型的关系[J]. 农业工程学报, 2017,33(6):232-239.
[5]	焦利民, 许刚, 赵素丽, 等. 基于LUR的武汉市PM_2.5浓度空间分布模拟[J]. 武汉大学学报(信息科学版), 2015,40(8):1088-1094.
[6]	刘炳杰, 彭晓敏, 李继红. 基于LUR模型的中国PM_2.5时空变化分析[J]. 环境科学, 2018,39(12):5296-5307.
[7]	许刚, 焦利民, 肖丰涛, 等. 土地利用回归模型模拟京津冀PM_2.5浓度空间分布[J]. 干旱区资源与环境, 2016,30(10):116-120.
[8]	李爽, 翟亮, 桑会勇, 等. 基于改进LUR模型的大区域PM_2.5浓度空间分布模拟[J]. 武汉大学学报(信息科学版), 2018,43(10):1574-1579.
[9]	赵佳楠, 徐建华, 卢德彬, 等. 基于RF-LUR模型的PM_2.5空间分布模拟:以长江三角洲地区为例[J]. 地理与地理信息科学, 2018,34(1):18-23.
[10]	生态环境部. 2017年中国生态环境状况公报[R].2018.
[11]	赵博娟,吴喜之. 非参数统计[M]. 4版. 北京:中国统计出版社, 2013.
[12]	ELITH J, LEATHWICK J R, HASTIE T. A working guide to boosted regression trees[J]. Journal of Animal Ecology, 2008,77(4):802-813.
[13]	PRASAD A M, IVERSON L R, LIAW A. Newer classification and regression tree techniques:bagging and random forests for ecological prediction[J]. Ecosystems, 2006,9(2):181-199.
[14]	MVLLER D L P S T. Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees[J]. Agricultural Systems, 2013,117:66-77.
[15]	蔡文华, 杨健, 刘志华, 等. 黑龙江省大兴安岭林区火烧迹地森林更新及其影响因子[J]. 生态学报, 2012,32(11):3303-3312.
[16]	李春林, 刘淼, 胡远满, 等. 基于增强回归树和Logistic回归的城市扩展驱动力分析[J]. 生态学报, 2014,34(3):727-737.
[17]	王迪, 王明新, 钱中平, 等. 基于增强回归树的农田总氮流失强度省际差异及其影响因素分析[J]. 生态与农村环境学报, 2017,33(12):1057-1063.
[18]	焦琳琳, 常禹, 申丹, 等. 利用增强回归树分析中国野火空间分布格局的影响因素[J]. 生态学杂志, 2015,34(8):2288-2296.
[19]	肖玉, 王硕, 李娜, 等. 北京城市绿地对大气PM_2.5的削减作用[J]. 资源科学, 2015,37(6):1149-1155.
[20]	代希腾, 弋紫阁, 姚广鹏. 旅游城市交通客流预测季节气候敏感性分析[J]. 交通与运输(学术版), 2015(增刊1):148-151.
[21]	万伟华. 土地利用变化对PM_2.5浓度的影响及空间效应研究[D]. 杭州:浙江大学, 2019.
[22]	吴健生, 马洪坤, 彭建. 基于"功能节点-关键廊道"的城市生态安全格局构建:以深圳市为例[J]. 地理科学进展, 2018,37(12):1663-1671.
[23]	葛跃, 王明新, 孙向武, 等. 基于增强回归树的城市PM_2.5日均值变化分析:以常州为例[J]. 环境科学, 2017,38(2):485-494.
[24]	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.
[25]	HENDERSON S B, BECKERMAN B, JERRETT M, et al. Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter[J]. Environmental Science & Technology, 2007,41(7):2422-2428.