Wang Zhen-bo, Fang Chuang-lin
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road Chaoyang District, Beijing 100101, China; Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China.
Chemosphere. 2016 Apr;148:148-62. doi: 10.1016/j.chemosphere.2015.12.118. Epub 2016 Jan 21.
Ambient particulate matter (PM) pollution of China has become a global concern and has great impact on air quality and human health. This paper adopts the PM2.5 concentration data obtained from 241 newly located observation points in the Bohai Rim Urban Agglomeration (BRUA), as well as economic, urban and industrial working population data in the study area, revealing the spatio-temporal distribution of PM2.5 and its determinants with the help of a spatial data model. The results indicate that: 1) The BRUA was the core area of PM2.5 pollution in China in 2014, the average PM2.5 concentration of which reached 74 μg/m(3), which is 13 μg/m(3) higher than the country average (61 μg/m(3)); 2) The PM2.5 concentration distribution had a characteristic of high in winter and autumn but low in spring and summer, presenting a U-shaped monthly profile and a U-impulse type daily profile; 3) The urban PM2.5 concentrations showed obvious spatial variation and agglomeration. The highest hot-spot was observed in spring, while the lowest was in summer. High concentration cities were mainly located in southern Hebei and western Shandong, and low concentration cities were in the coastal area around the Bohai Sea and the mountainous areas in northern Hebei. High hot-spot areas demonstrated an M-shaped change, with two cycles of advance and retreat from west to east. 4) The Geographically weighted regression (GWR) model shows that the GDP per capita, urbanization rate and construction of the cities were closely related to PM2.5 concentrations in the BRUA.
中国的大气颗粒物(PM)污染已成为全球关注的问题,对空气质量和人类健康产生了重大影响。本文采用了从环渤海城市群(BRUA)241个新设立的观测点获取的PM2.5浓度数据,以及研究区域内的经济、城市和产业从业人口数据,借助空间数据模型揭示了PM2.5的时空分布及其影响因素。结果表明:1)2014年BRUA是中国PM2.5污染的核心区域,其PM2.5平均浓度达到74μg/m³,比全国平均水平(61μg/m³)高13μg/m³;2)PM2.5浓度分布呈现冬秋季高、春夏季低的特征,月变化呈U形,日变化呈U脉冲型;3)城市PM2.5浓度呈现明显的空间差异和集聚性。春季观测到的热点最高,夏季最低。高浓度城市主要位于河北南部和山东西部,低浓度城市位于渤海沿岸地区和河北北部山区。高热区呈现M形变化,有两个从西向东进退的周期;4)地理加权回归(GWR)模型表明,BRUA中人均GDP、城市化率和城市建设与PM2.5浓度密切相关。