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Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

Mercer, Laina D.; Szpiro, Adam A.; Sheppard, Lianne; Lindström, Johan LU ; Adar, Sara D.; Allen, Ryan W.; Avol, Edward L.; Oron, Assaf P.; Larson, Timothy and Liu, L. -J. Sally, et al. (2011) In Atmospheric Environment 45(26). p.4412-4420
Abstract
Background: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land-use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. Methods: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were... (More)
Background: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land-use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. Methods: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R-2 and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. Results: UK models consistently performed as well as or better than the analogous LUR models. The best CV R-2 values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R-2 values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R-2 values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. Conclusion: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK. (C) 2011 Elsevier Ltd. All rights reserved. (Less)
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keywords
Universal kriging, Land-use regression, Spatial modeling, Air, pollution, Exposure assessment, Los Angeles
in
Atmospheric Environment
volume
45
issue
26
pages
4412 - 4420
publisher
Elsevier
external identifiers
  • wos:000293680100010
  • scopus:79959912501
ISSN
1352-2310
DOI
10.1016/j.atmosenv.2011.05.043
project
MERGE
BECC
language
English
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yes
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d91a6d79-fc41-4359-8e57-a97a0424b0f2 (old id 2161542)
date added to LUP
2011-09-21 12:38:07
date last changed
2017-10-01 04:24:30
@article{d91a6d79-fc41-4359-8e57-a97a0424b0f2,
  abstract     = {Background: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land-use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. Methods: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R-2 and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. Results: UK models consistently performed as well as or better than the analogous LUR models. The best CV R-2 values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R-2 values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R-2 values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. Conclusion: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK. (C) 2011 Elsevier Ltd. All rights reserved.},
  author       = {Mercer, Laina D. and Szpiro, Adam A. and Sheppard, Lianne and Lindström, Johan and Adar, Sara D. and Allen, Ryan W. and Avol, Edward L. and Oron, Assaf P. and Larson, Timothy and Liu, L. -J. Sally and Kaufman, Joel D.},
  issn         = {1352-2310},
  keyword      = {Universal kriging,Land-use regression,Spatial modeling,Air,pollution,Exposure assessment,Los Angeles},
  language     = {eng},
  number       = {26},
  pages        = {4412--4420},
  publisher    = {Elsevier},
  series       = {Atmospheric Environment},
  title        = {Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)},
  url          = {http://dx.doi.org/10.1016/j.atmosenv.2011.05.043},
  volume       = {45},
  year         = {2011},
}