A spatiotemporal land-use-regression model to assess individual level long-term exposure to ambient fine particulate matters
A spatiotemporal land-use-regression model to assess individual level long-term exposure to ambient fine particulate matters
复制标题
用于评估个人水平长期暴露于环境细颗粒物的时空土地利用回归模型
DOI：
10.1016/j.mex.2019.09.009
复制
发表时间：
2019-01-01
期刊：
影响因子：
1.9
通讯作者：
Ma, Wenjun
中科院分区：
文献类型：
--
作者：
Liu, Tao;Xiao, Jianpeng;Ma, Wenjun
我们的目的是建立一个时空土地利用回归（ST-LUR）模型，评估2015年至2016年在中国广东省登记的6627名成年人中细颗粒物（PM2. 5）的个人水平长期暴露。我们收集了2013年4月至2016年12月期间每周平均PM2.5浓度（来自空气质量监测站）以及每个空气质量监测站的能见度、人口密度、道路密度和土地用途类型以及参与者的居住地址。利用这些时空数据建立了一个ST-LUR模型，并采用该模型估计了每个居民地址的每周平均PM2. 5浓度。采用R软件（3.5.1版）和SpatioTemporal软件包进行数据分析。结果表明，ST-LUR模型应用的土地利用数据提取的缓冲半径为1300米具有最好的建模拟合。10倍交叉验证的结果表明，R-2为88.86%，RMSE（均方根误差）为5.65 μ g/m3。计算了每位参与者在调查日期前两年的PM2.5平均值。本研究提供了一种新的方法来精确评估个人水平的长期暴露于环境PM2.5，这可能会扩大我们对空气污染的健康影响的理解。时空土地利用回归（ST-LUR）模型的变量输入包括能见度，人口密度，道路密度，ST-LUR模型的R-2为88.86%，RMSE为5.65 μ g/m3，表明该模型具有良好的性能。(C)2019年，任作家。由爱思唯尔公司出版
We aimed to establish a spatiotemporal land-use-regression (ST-LUR) model assessing individual level long-term exposure to fine particulate matters (PM2.5) among 6627 adults enrolled in Guangdong province, China from 2015 to 2016. We collected weekly average PM2.5 concentration (from the air quality monitoring stations) and visibility, population density, road density and types of land use of each air quality monitoring station and participant's residential address from April 2013 to December 2016. A ST-LUR model was established using these spatiotemporal data, and was employed to estimate the weekly average PM2.5 concentration of each individual residential address. Data analysis was carried out by R software (version 3.5.1) and the SpatioTemporal package was used. The results showed that the ST-LUR model applying the land use data extracted using a buffer radius of 1300 m had the best modelling fitness. The results of 10-fold cross validation showed that the R-2 was 88.86% and the RMSE (Root mean square error) was 5.65 mu,g/m(3). The two-year average of PM2.5 prior to the date of investigation were calculated for each participant. This study provided a novel method to precisely assess individual level long-term exposure to ambient PM2.5, which may extend our understanding on the health impacts of air pollution.Variables input in the spatiotemporal land-use-regression (ST-LUR) model include visibility, population density, road density, and types of land use.The land use data should be extracted using a buffer radius of 1300 m.The R-2 of the ST-LUR model was 88.86% and the RMSE was 5.65 mu g/m(3), indicating the good performance of the model. (C) 2019 The Authors. Published by Elsevier B.V.