Evaluating methods for spatial mapping: Applications for estimating ozone concentrations across the contiguous United States

Jesse D. Berman, Patrick N Breysse, Ronald White, Darryn W. Waugh, Frank C Curriero

Research output: Contribution to journalArticle

Abstract

Understanding spatial variability of air pollutant concentrations is critical for public health assessments. Our goal is to examine ground-level ozone and comparatively evaluate method performance for predicting and mapping national concentrations across the United States, while assessing the importance of accounting for spatial variability.Cross-sectional US EPA ozone monitoring data was acquired for three days in 2006, plus environmental covariates of land use, traffic, temperature, elevation, and population. Evaluation of ozone variability was assessed with land use regression (LUR) and spatially explicit kriging models. Ozone concentration was predicted with four approaches, including LUR, inverse distance weighting (IDW), ordinary kriging, and universal kriging, and evaluated with a Monte Carlo cross-validation simulation. Results were mapped for the continental United States.Temperature, elevation, and distance to major roads were significantly related to ozone concentrations and examination of spatial dependence on LUR models revealed the presence of residual spatial variation. Cross-validation results found kriging outperformed both LUR and IDW in terms of root mean squared prediction error. We demonstrate that national-level ozone is best evaluated using the statistically optimal kriging models, which account for residual spatial variation. Universal kriging was preferred over ordinary kriging by allowing us to assess the significance of environmental covariates both for inference and prediction of ozone concentrations.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalEnvironmental Technology and Innovation
Volume3
DOIs
StatePublished - Apr 1 2015

Fingerprint

kriging
ozone
land use
methodology
spatial variation
prediction
United States Environmental Protection Agency
method
traffic
roads
public health
temperature
pollutants
road
air
monitoring
simulation

Keywords

  • Kriging
  • Mapping
  • Method comparison
  • Ozone
  • Spatial prediction

ASJC Scopus subject areas

  • Plant Science
  • Soil Science
  • Environmental Science(all)

Cite this

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title = "Evaluating methods for spatial mapping: Applications for estimating ozone concentrations across the contiguous United States",
abstract = "Understanding spatial variability of air pollutant concentrations is critical for public health assessments. Our goal is to examine ground-level ozone and comparatively evaluate method performance for predicting and mapping national concentrations across the United States, while assessing the importance of accounting for spatial variability.Cross-sectional US EPA ozone monitoring data was acquired for three days in 2006, plus environmental covariates of land use, traffic, temperature, elevation, and population. Evaluation of ozone variability was assessed with land use regression (LUR) and spatially explicit kriging models. Ozone concentration was predicted with four approaches, including LUR, inverse distance weighting (IDW), ordinary kriging, and universal kriging, and evaluated with a Monte Carlo cross-validation simulation. Results were mapped for the continental United States.Temperature, elevation, and distance to major roads were significantly related to ozone concentrations and examination of spatial dependence on LUR models revealed the presence of residual spatial variation. Cross-validation results found kriging outperformed both LUR and IDW in terms of root mean squared prediction error. We demonstrate that national-level ozone is best evaluated using the statistically optimal kriging models, which account for residual spatial variation. Universal kriging was preferred over ordinary kriging by allowing us to assess the significance of environmental covariates both for inference and prediction of ozone concentrations.",
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AU - Berman, Jesse D.

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AU - White, Ronald

AU - Waugh, Darryn W.

AU - Curriero, Frank C

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N2 - Understanding spatial variability of air pollutant concentrations is critical for public health assessments. Our goal is to examine ground-level ozone and comparatively evaluate method performance for predicting and mapping national concentrations across the United States, while assessing the importance of accounting for spatial variability.Cross-sectional US EPA ozone monitoring data was acquired for three days in 2006, plus environmental covariates of land use, traffic, temperature, elevation, and population. Evaluation of ozone variability was assessed with land use regression (LUR) and spatially explicit kriging models. Ozone concentration was predicted with four approaches, including LUR, inverse distance weighting (IDW), ordinary kriging, and universal kriging, and evaluated with a Monte Carlo cross-validation simulation. Results were mapped for the continental United States.Temperature, elevation, and distance to major roads were significantly related to ozone concentrations and examination of spatial dependence on LUR models revealed the presence of residual spatial variation. Cross-validation results found kriging outperformed both LUR and IDW in terms of root mean squared prediction error. We demonstrate that national-level ozone is best evaluated using the statistically optimal kriging models, which account for residual spatial variation. Universal kriging was preferred over ordinary kriging by allowing us to assess the significance of environmental covariates both for inference and prediction of ozone concentrations.

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