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Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer
06:27

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Published on: May 29, 2019

High Resolution Space-Time Ozone Modeling for Assessing Trends.

Sujit K Sahu1, Alan E Gelfand, David M Holland

  • 1School of Mathematics, Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK.

Journal of the American Statistical Association
|September 18, 2009
PubMed
Summary
This summary is machine-generated.

A new space-time model helps assess air pollution control programs by predicting ground-level ozone levels and trends. This model aids environmental managers in evaluating program effectiveness across different areas.

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Last Updated: Jun 20, 2026

Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer
06:27

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Published on: May 29, 2019

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Statistical Modeling

Background:

  • Assessing air pollution control programs requires understanding ground-level ozone behavior.
  • Accurate ozone modeling is crucial for predicting spatial summaries and detecting long-term trends.

Purpose of the Study:

  • To propose a novel space-time model for daily 8-hour maximum ozone levels.
  • To aid regulatory activities in detecting, evaluating, and analyzing ozone patterns and trends.

Main Methods:

  • Developed an auto-regressive space-time model incorporating meteorological variables.
  • Addressed data misalignment using spatial modeling of meteorological data increments.
  • Employed a Bayesian framework and Markov Chain Monte Carlo (MCMC) techniques for model fitting.

Main Results:

  • The model was applied to ozone data from Ohio, encompassing urban, suburban, and rural sites.
  • The approach effectively models space-time ozone behavior and incorporates meteorological influences.
  • Full inference for model unknowns and predictions in time and space is achievable.

Conclusions:

  • The proposed space-time model provides valuable input for air pollution regulatory assessments.
  • The model facilitates the evaluation of pollution control program effectiveness by analyzing ozone trends.
  • This statistical approach enhances the understanding of ozone dynamics and spatial patterns.