自然资源学报 ›› 2021, Vol. 36 ›› Issue (4): 1036-1046.doi: 10.31497/zrzyxb.20210417

• 其他研究论文 • 上一篇    下一篇

华北地区颗粒物浓度时空分布特征及其因素

詹建益1(), 黄观超2, 周华3, 段文松2(), 吴安安2, 王文洁2, 李婷2   

  1. 1.浙江建设职业技术学院,杭州 311231
    2.安徽师范大学环境科学与工程学院,芜湖 241000
    3.安徽师范大学地理与旅游学院,芜湖 241000
  • 收稿日期:2019-10-13 修回日期:2020-05-06 出版日期:2021-04-28 发布日期:2021-06-28
  • 通讯作者: 段文松(1979- ),男,安徽芜湖人,博士,教授,研究方向为水体整治与生态修复。E-mail: dws7911@163.com
  • 作者简介:詹建益(1980- ),男,浙江杭州人,硕士,讲师,研究方向为环境治理和生态修复。E-mail: zhanjianyi@126.com
  • 基金资助:
    安徽省自然科学基金项目(1608085MB45)

Spatial and temporal distribution characteristics and factors of particulate matter concentration in North China

ZHAN Jian-yi1(), HUANG Guan-chao2, ZHOU Hua3, DUAN Wen-song2(), WU An-an2, WANG Wen-jie2, LI Ting2   

  1. 1. Zhejiang College of Construction, Hangzhou 311231, China
    2. School of Environmental Science and Engineering, Anhui Normal University, Wuhu 241000, Anhui, China
    3. School of Geography and Tourism, Anhui Normal University, Wuhu 241000, Anhui, China
  • Received:2019-10-13 Revised:2020-05-06 Online:2021-04-28 Published:2021-06-28

摘要:

利用2017年华北地区各地级及以上城市空气质量浓度等有关数据,对该区域内的颗粒物浓度时空分布特征进行研究,在此基础上进一步利用空间自相关分析方法对该区域内的颗粒物浓度的空间聚集特征进行定量描述,并利用空间计量模型分析了影响华北地区城市颗粒物浓度的因素。结果表明:整体上,华北地区PM2.5和PM10的污染日出现的平均频率分别为17.25%和14.23%,需重点关注细颗粒物造成的污染。在时间分布上,各省市的颗粒物月均浓度存在“U”型变化,呈现出冬季>秋季≈春季>夏季的规律。在空间分布上,各地级市颗粒物年均浓度具有明显的空间聚集特性,高聚集主要出现在河北南部,低聚集主要出现在内蒙古。空间计量模型表明,风速、降雨量和人均GDP对华北地区城市的PM2.5和PM10年均浓度均具有显著的负向影响,而第二产业占比、煤炭使用量和机动车保有量均对颗粒物浓度有正向影响,其中煤炭消耗量的影响最大,其次是机动车保有量。上述研究结果可为制定华北地区大气污染控制提供有效的措施和科学依据。

关键词: 华北地区, 颗粒物浓度, 时空分布, 空间自相关, 空间计量模型

Abstract:

Based on the data of air quality concentration of cities in North China in 2017, the spatial and temporal distribution characteristics of particle concentration in this region are studied. On this basis, the spatial autocorrelation analysis method is used to quantitatively describe the spatial aggregation characteristics of particle concentration in the region, and the spatial measurement model is used to analyze the particle concentration affected cities in North China. The results show that: on the whole, the average frequency of PM2.5 and PM10 pollution is 17.25% and 14.23% respectively in the study area. In terms of temporal distribution, there is a "U"-shaped change in the monthly average concentration of particulate matter in all provinces and cities, the average concentration has obvious seasonal changes, showing a pattern of winter > autumn ≈ spring > summer. In terms of spatial distribution, the annual average concentration of particulate matter in different prefecture-level cities has obvious aggregation characteristics. The high concentration mainly occurs in the south of Hebei province, while the low concentration mainly appears in Inner Mongolia. The spatial econometric model shows that wind speed, rainfall and GDP per capita have a significant negative impact on the average annual concentration of PM2.5 and PM10 of cities in North China, while the proportion of secondary industry, coal use and the number of vehicles have a positive impact on the concentration of particulate matter, among which coal consumption has the largest impact, followed by the number of vehicles. The above results can provide scientific basis for formulating policies for air pollution control in North China.

Key words: North China, particulate matter concentration, spatial and temporal distribution, spatial autocorrelation, spatial econometric model