Hourly Ozone Levels Research Articles

Estimating contribution of wildland fires to ambient ozone levels in National Parks in the Sierra Nevada, California

Data from four continuous ozone and weather monitoring sites operated by the National Park Service in Sierra Nevada, California, are used to develop an ozone forecasting model and to estimate the contribution of wildland fires on ambient ozone levels. The analyses of weather and ozone data pointed to the transport of ozone precursors from the Central Valley as an important source of pollution in these National Parks. Comparisons of forecasted and observed values demonstrated that accurate forecasts of next-day hourly ozone levels may be achieved by using a time series model with historic averages, expected local weather and modeled PM values as explanatory variables. Results on fire smoke influence indicated occurrence of significant increases in average ozone levels with increasing fire activity. The overall effect on diurnal ozone values, however, was small when compared with the amount of variability attributed to sources other than fire.

Lisbon air quality forecast using statistical methods

Ozone and particulate matter levels in Southern European countries are particularly high, exceeding the established limit values, and the information and alert thresholds (in the case of ozone). Therefore, it is relevant to develop a good prediction methodology for the concentrations of these pollutants. Statistical models based on Multiple Regression (MR) analysis and classification and regression trees analysis were developed successfully. The models were applied in forecasting the average daily concentrations for particulate matter and average maximum hourly ozone levels, for next day, for the group of existing air quality monitoring stations in the Metropolitan Area of North Lisbon in Portugal.

Is the association of airborne particles with daily deaths confounded by gaseous air pollutants? An approach to control by matching.

Although particulate air pollution has been associated with increased numbers of daily deaths in dozens of cities around the world, issues still remain about the association. Some have questioned the complex modeling used to control for season in Poisson regression or the role of gaseous air pollutants as potential confounders of the association. I examined the association between deaths and particulate matter with an aerodynamic diameter less than or equal to 10 microm (PM10) using a case-crossover design. In this approach, the pollution on the day of each death is contrasted with the pollution level on control days when the subject did not die. Season and gaseous air pollutants were controlled by matching. Control days were chosen within the same month of the same year to control for season, and matched on either sulfur dioxide (SO2; within 1 ppb), nitrogen dioxide (within 1 ppb), maximum ozone (within 2 ppb), or carbon monoxide (within 0.03 ppm). The analysis was conducted in 14 U.S. cities that have daily PM10 monitoring. After matching, there were about 400,000 deaths in each analysis. Results were combined across cities using a maximum likelihood method. PM10 was a significant predictor of mortality when controlling for gaseous air pollutants, with effect sizes ranging from a 0.45% increase per 10 microg/m3 increment of PM10 [95% confidence interval (CI), 0.12-0.79%] when matched on maximum hourly ozone levels, to a 0.81% increase per 10 microg/m3 increment of PM10 (95% CI, 0.47-1.16%) when matched on 24-hr average SO2.

Space-time estimation of grid-cell hourly ozone levels for assessment of a deterministic model

We present an approach to estimate hourly grid-cell surface ozone concentrations based on observations from point monitoring sites in space, for comparison with grid-based results from the SARMAP photochemical air-quality model for a region of northern California. Statistical estimation is carried out on a transformed (square root) scale, followed by back-transforming to the original scale of ozone in parts per billion, adjusting for bias and variance. We estimate a spatially-varying diurnal mean structure and a non-separable space-time correlation structure on the transformed scale. Temporal pre-whitening is followed by modelling of a spatially non-stationary, diurnally-varying spatial correlation structure using a spatial deformation approach. Comparisons of SARMAP model results with the estimated grid-cell ozone levels are presented.