摘要
在我国快速的城市化进程中,快速的经济发展和日益增加的能源消耗带来的大气污染不断增加,特别是细颗粒物污染如PM2.5污染越来越严重,PM2.5污染相关研究成为一个热点议题.高浓度的PM2.5是形成我国京津冀、珠三角和长三角地区大气灰霾的主要原因,大气污染已成为制约京津冀地区乃至全国可持续发展的关键问题,长期暴露在PM2.5大气污染中,会对人类健康造成诸多不良影响.土地利用回归模型可以实现大气污染物浓度的时空模拟,明晰PM2.5浓度的空间分布特征对于大气污染的防治和流行病学的研究具有重要意义.本研究利用2014年1月1日至2014年12月31日京津冀地区104个监测站点的大气污染物浓度数据,结合VIIRS(visible infrared imaging radiometer)AOD(aerosol optical depth)、土地覆被、气象因子、道路分布、人口密度、污染源分布等信息,分别利用最小二乘和地理加权回归构建土地利用回归模型,对PM2.5浓度时空分布情况进行模拟,其中包括含VIIRS AOD数据的最小二乘土地利用模型和地理加权土地利用模型,以及不包含VIIRS AOD数据的最小二乘土地利用模型和地理加权土地利用模型,这4个模型的修正R2值分别为82.13%、84.87%、80.45%和81.99%.研究表明,相比最小二乘回归,使用地理加权回归的方法能一定程度上提升土地利用回归模型的结果.
ue to the rapid urbanization and increasing energy consumption, air pollution, especially some fine particulates like PM2.5 rise in the context of fast urbanization. PM2.5 pollution has been given considerable attention recent years. High PM2.5 concentration is the main reason for the atmospheric haze in Beijing-Tianjin-Hebei region. Air pollution has become the key issue restricting the sustainable development of Beijing-Tianjin-Hebei region and even the whole country. Long-term exposure to PM2.5 is likely to cause adverse effects on human health. The spatial-temporal variation of air pollution can be characterized by the land use regression model. It is significant to have a good knowledge of spatial characteristics of PM2.5 concentration, which could assist air pollution management and the epidemiological research. This manuscript used air quality data of 104 monitoring sites of Beijing-Tianjin-Hebei region from 1st January 2014 to 31st December, 2014, combined with VⅡRS （visible infrared imaging radiometer） AOD（aerosol optical depth）, land use, meteorological factors, road network, population, and pollutant sources distribution to establish the land use regression model by least square method and geographically weighted method respectively. The four models established were least square land use regression model with VⅡRS AOD data, geographically weighted land use regression model with VⅡRS AOD data, least square land use regression model without VⅡRS AOD data and geographically weighted land use regression model without VⅡRS AOD data. And the adjusted R2 values for these four models were 82.13%, 84.87%, 80.45% and 81.99%, respectively. Research results demonstrated that the geographically weighted method performed better than the least square method and improved the land use regression model to a certain extent.
出处
《环境科学》
EI
CAS
CSCD
北大核心
2017年第6期2191-2201,共11页
Environmental Science
基金
国家自然科学基金重点项目(41330747)
深圳科技创新项目(JCYJ20140903101902349)
