Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

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)

In English

Författare

  • Laina D. Mercer
  • Adam A. Szpiro
  • Lianne Sheppard
  • Johan Lindström
  • Sara D. Adar
  • Ryan W. Allen
  • Edward L. Avol
  • Assaf P. Oron
  • Timothy Larson
  • L. -J. Sally Liu
  • Joel D. Kaufman
Visa allt

Summary, in English

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.

Publiceringsår

2011

Språk

Engelska

Sidor

4412-4420

Publikation/Tidskrift/Serie

Atmospheric Environment

Volym

45

Issue

26

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Meteorology and Atmospheric Sciences

Nyckelord

  • Universal kriging
  • Land-use regression
  • Spatial modeling
  • Air
  • pollution
  • Exposure assessment
  • Los Angeles

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1352-2310