Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model

Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model

Excessive exposure to ambient (outdoor) air pollution may greatly increase the incidences of respiratory and cardiovascular diseases. Accurate reports of the spatial-temporal distribution characteristics of daily PM2.5 exposure can effectively prevent and reduce the harm caused to humans. Based on t...

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Main Authors: Jinhuang Lin, An Zhang, Wenhui Chen, Mingshui Lin
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Sustainability
Subjects:
spatio-temporal kriging
PM2.5 exposure
BM model
cumulative duration
Beijing
Online Access:http://www.mdpi.com/2071-1050/10/8/2772
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spelling doaj-art-82704e9c119f407cbe0504e56ea3cd7b2018-08-22T08:09:47ZengMDPI AGSustainability2071-10502018-08-01108277210.3390/su10082772su10082772Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging ModelJinhuang Lin0An Zhang1Wenhui Chen2Mingshui Lin3College of Geographical Science, Fujian Normal University, Fuzhou 350007, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Geographical Science, Fujian Normal University, Fuzhou 350007, ChinaCollege of Tourism, Fujian Normal University, Fuzhou 350117, ChinaExcessive exposure to ambient (outdoor) air pollution may greatly increase the incidences of respiratory and cardiovascular diseases. Accurate reports of the spatial-temporal distribution characteristics of daily PM2.5 exposure can effectively prevent and reduce the harm caused to humans. Based on the daily average concentration data of PM2.5 in Beijing in May 2014 and the spatio-temporal kriging (STK) theory, we selected the optimal STK fitting model and compared the spatial-temporal prediction accuracy of PM2.5 using the STK method and ordinary kriging (OK) method. We also reveal the spatial-temporal distribution characteristics of the daily PM2.5 exposure in Beijing. The results show the following: (1) The fitting error of the Bilonick model (BM) model which is the smallest (0.00648), and the fitting effect of the prediction model of STK is the best for daily PM2.5 exposure. (2) The cross-examination results show that the STK model (RMSE = 8.90) has significantly lower fitting errors than the OK model (RMSE = 10.70), so its simulation prediction accuracy is higher. (3) According to the interpolation of the STK model, the daily exposure of PM2.5 in Beijing in May 2014 has good continuity in both time and space. The overall air quality is good, and overall the spatial distribution is low in the north and high in the south, with the highest concentration in the southwestern region. (4) There is a certain degree of spatial heterogeneity in the cumulative duration at the good, moderate, and polluted grades of China National Standard. The areas with the longest cumulative duration at the good, moderate and polluted grades are in the north, southeast, and southwest of the study area, respectively.http://www.mdpi.com/2071-1050/10/8/2772spatio-temporal krigingPM2.5 exposureBM modelcumulative durationBeijing
institution Open Data Bank
collection Open Access Journals
building Directory of Open Access Journals
language English
format Article
author Jinhuang Lin
An Zhang
Wenhui Chen
Mingshui Lin
spellingShingle Jinhuang Lin
An Zhang
Wenhui Chen
Mingshui Lin
Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model
Sustainability
spatio-temporal kriging
PM2.5 exposure
BM model
cumulative duration
Beijing
author_facet Jinhuang Lin
An Zhang
Wenhui Chen
Mingshui Lin
author_sort Jinhuang Lin
title Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model
title_short Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model
title_full Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model
title_fullStr Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model
title_full_unstemmed Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model
title_sort estimates of daily pm2.5 exposure in beijing using spatio-temporal kriging model
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2018-08-01
description Excessive exposure to ambient (outdoor) air pollution may greatly increase the incidences of respiratory and cardiovascular diseases. Accurate reports of the spatial-temporal distribution characteristics of daily PM2.5 exposure can effectively prevent and reduce the harm caused to humans. Based on the daily average concentration data of PM2.5 in Beijing in May 2014 and the spatio-temporal kriging (STK) theory, we selected the optimal STK fitting model and compared the spatial-temporal prediction accuracy of PM2.5 using the STK method and ordinary kriging (OK) method. We also reveal the spatial-temporal distribution characteristics of the daily PM2.5 exposure in Beijing. The results show the following: (1) The fitting error of the Bilonick model (BM) model which is the smallest (0.00648), and the fitting effect of the prediction model of STK is the best for daily PM2.5 exposure. (2) The cross-examination results show that the STK model (RMSE = 8.90) has significantly lower fitting errors than the OK model (RMSE = 10.70), so its simulation prediction accuracy is higher. (3) According to the interpolation of the STK model, the daily exposure of PM2.5 in Beijing in May 2014 has good continuity in both time and space. The overall air quality is good, and overall the spatial distribution is low in the north and high in the south, with the highest concentration in the southwestern region. (4) There is a certain degree of spatial heterogeneity in the cumulative duration at the good, moderate, and polluted grades of China National Standard. The areas with the longest cumulative duration at the good, moderate and polluted grades are in the north, southeast, and southwest of the study area, respectively.
topic spatio-temporal kriging
PM2.5 exposure
BM model
cumulative duration
Beijing
url http://www.mdpi.com/2071-1050/10/8/2772
_version_ 1612678667660427264

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