TY - JOUR
T1 - A hybrid kriging/land-use regression model to assess PM2.5 spatial-temporal variability
AU - Wu, Chih Da
AU - Zeng, Yu Ting
AU - Lung, Shih Chun Candice
N1 - Funding Information:
This work was supported by the National Health Researc Institute ( NHRI-106A1-PDCO-3217181 ) and the Academia Sinica ( AS-104-SS-A02 ). We appreciate data support from the Environmental Protection Administration, Ministry of Executive Yuan, Taiwan. We also thank the anonymous reviewers for their constructive comments on the early manuscript of this paper. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the funding agencies. The funding agencies had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2018
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/12/15
Y1 - 2018/12/15
N2 - Proximate pollutant data can provide information for land-use predictors in LUR models, when coupled with spatial interpolation of ambient pollutant measurements, may provide better pollutant predictions. This study applies a hybrid kriging/LUR model to assess the spatial-temporal variability of PM2.5 for Taiwan. Using PM2.5 concentrations at 71 EPA monitoring stations from 2006 to 2011, pollutant gradient surfaces were spatially interpolated using a leave-one-out ordinary kriging method based on “n-1” observations. The predicted concentration level of the targeted site was then extracted from the generated kriging map and adopted as a variable in LUR modelling. Annual and monthly resolutions of LUR models were developed to assess the effects by incorporating kriging-based estimates into pollutant predictions. The R2 obtained from conventional LUR procedures was 0.66 and 0.70 for annual and monthly models, respectively, whereas models using the hybrid approach showed better explanatory power (R2 of annual model: 0.85; R2 of monthly model: 0.88). Moreover, kriging-based PM2.5 estimates were the most important factor in the resultant models according to the dominant partial R2 of 0.82 and 0.7 in monthly and yearly models. Cross-validation and external data verification showed similar results, demonstrating robustness of the proposed approach. Using governmental pollutant observations is usually publicly available for most areas, this method provides an efficient mean to better assess PM2.5 spatial-temporal variations and predicts levels for nonmonitored areas.
AB - Proximate pollutant data can provide information for land-use predictors in LUR models, when coupled with spatial interpolation of ambient pollutant measurements, may provide better pollutant predictions. This study applies a hybrid kriging/LUR model to assess the spatial-temporal variability of PM2.5 for Taiwan. Using PM2.5 concentrations at 71 EPA monitoring stations from 2006 to 2011, pollutant gradient surfaces were spatially interpolated using a leave-one-out ordinary kriging method based on “n-1” observations. The predicted concentration level of the targeted site was then extracted from the generated kriging map and adopted as a variable in LUR modelling. Annual and monthly resolutions of LUR models were developed to assess the effects by incorporating kriging-based estimates into pollutant predictions. The R2 obtained from conventional LUR procedures was 0.66 and 0.70 for annual and monthly models, respectively, whereas models using the hybrid approach showed better explanatory power (R2 of annual model: 0.85; R2 of monthly model: 0.88). Moreover, kriging-based PM2.5 estimates were the most important factor in the resultant models according to the dominant partial R2 of 0.82 and 0.7 in monthly and yearly models. Cross-validation and external data verification showed similar results, demonstrating robustness of the proposed approach. Using governmental pollutant observations is usually publicly available for most areas, this method provides an efficient mean to better assess PM2.5 spatial-temporal variations and predicts levels for nonmonitored areas.
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U2 - 10.1016/j.scitotenv.2018.07.073
DO - 10.1016/j.scitotenv.2018.07.073
M3 - Article
C2 - 30248867
AN - SCOPUS:85050186490
VL - 645
SP - 1456
EP - 1464
JO - Science of the Total Environment
JF - Science of the Total Environment
SN - 0048-9697
ER -