Data on ambient ozone concentrations are typically measured near urban areas with large populations. Rural ozone monitoring is sparse but the number of rural ozone monitoring has increased in the past 10 years. Natural resource managers need to use the available ozone data and extrapolate the measured values to estimate the seasonal ozone exposure, along with the uncertainties in the estimates, to National Forest lands. Two ozone exposure statistics are of interest to estimate because when they are combined the information can be used, along with soil moisture data (Lefohn et al., 1997), to predict where vegetation has the greatest risk from suffering from biomass (growth) reductions. The two exposure statistics used to estimate the biomass loss are the N100 and the W126. The N100 is the number of hours when the measured ozone concentration is greater than or equal to 0.100 parts per million (ppm). Experimental trials with a frequent number of peaks (hourly averages greater than or equal to 0.100 ppm) have been demonstrated to cause greater growth loss to vegetation than trials with no peaks in the exposure regime (Hogsett et al., 1985; Musselman et al., 1983; Musselman et al., 2006; and Musselman et al., 1986). The second statistic is the seasonal ozone exposure called the W126 (Lefohn and Runeckles, 1987). The W126 was developed as a biologically meaningful way to summarize hourly average ozone data. The W126 places a greater weight on the measured values as the concentrations increase. Thus, it is possible for a high W126 value to occur with few to no hours above 0.100 ppm. Therefore, it is also necessary to determine the number of hours the ozone concentrations are greater than or equal to 0.100 ppm. It should also be noted the lack of N100 values does not mean ozone symptoms will not be present when field surveys are conducted. The use of both the N100 and W126 is consistent with the recommendations of the Federal Land Manager Air Quality Related Values Workgroup (FLAG, 2002).
The USDA Forest Service has acquired ozone data for particular years of interest from all the available monitoring sites (with an acceptable percent data capture) in the lower 48 United States. Three distinct types of spatial data have been produced. These include: 1) a summary of the data at each of the ambient monitoring locations 2) estimates of the W126 and N100, along with the uncertainties, using ordinary kriging (Knudsen and Lefohn, 1988; and Lefohn et al., 1988), and 3) raster files of the W126, N100, and the 95 percent confidence interval for each of the preceding estimates.
- Percent data capture
- Uncorrected W126
- Corrected W126
- Uncorrected N100
- Corrected N100
- 4th highest 8-hour maximum
- 2nd highest hourly average
- Elevation from 90 m DEM
The values for the corrected W126 and N100 are used in the ordinary kriging analysis. Interpolation techniques are employed so that all of the monitoring site used in the analysis have 100 percent data capture.
The corrected W126 and N100 values are used as input into a ordinary kriging model analysis. Estimates represent the seasonal (using 24-hour concentrations for April through September) W126 and N100 for each point spaced at 0.5 degrees latitude by 0.5 degrees longitude. The values are stored in a database (.mdb file format) and also include the monitoring summary noted above. The W126 and N100 tables in the database include:
- W126 or N100 estimate
- 95 percent confidence interval
- Number of monitors used in the estimate
The kriging results for the W126, N100, and the 95 percent confidence interval for each were then exported as a raster grid where each of the cells represents approximately 0.5 degrees latitude by 0.5 degrees longitude.
The following table lists the files available for download. The monitoring results shapefile and the W126 and N100 database are stored as executable files. The default directory for these files is c:\ozone_spatial\'year' unless the directory is changed. The raster files are also stored as executable files that will extract to c:\ozone_spatial\'year'. All of the raster files are in interchange file format (.e00) and must be imported to build the raster coverage, except for the results for 2010. The 2010 results are saved as executable Zip files and the rasters will be downloaded to the directory of your choosing.
|W126 and N100
* Includes spatial coverages for both the newer 95 percent confidence interval described by Yamamoto (2005) and the older method described by Lefohn et al. (1988).
FLAG. 2002. Federal Land Managers' Air Quality Related Values Workgroup. Phase I Report. December 2000.
Hogsett, W. E.; Plocher M; Wildman V.; Tingey, D. T. and Bennett, J. P. 1985. Growth response of two varieties of slash pine seedlings to chronic ozone exposures. Can. J. Botany 63:2369-2376.
Knudsen, H.P. and A.S. Lefohn. 1988. The Use of Spatial Statistics to Characterize Regional Ozone Exposures, pp 91-105. In: Assessment of Crop Loss from Air Pollutants. W.W. Heck, O.C. Taylor, D.T. Tingey (eds). Elsevier Applied Science Publishing, London, U.K.
Lefohn, A. S.;Runeckles, V. C. 1987. Establishing a standard to protect vegetation - ozone exposure/dose considerations. Atmos. Environ. 21:561-568.
Lefohn, A.S., H.P. Knudsen, and L.R. McEvoy, Jr. 1988. The Use of Kriging to Estimate Monthly Ozone Exposure Parameters for the Southeastern United States. Environmental Pollution. 53:27-42.
Lefohn, A.S.; Jackson, W.; Shadwick, D.S.; Knudsen, H.P. 1997. Effect of surface ozone exposures on vegetation grown in the Southern Appalachian Mountains: Identification of possible areas of concern. Atmos. Environ. 31: 695-1708.
Musselman, R. C.; Oshima, R. J. and Gallavan, R. E. 1983. Significance of pollutant concentration distribution in the response of 'red kidney' beans to ozone. J. Am. Soc. Hort. Sci. 108:347-351.
Musselman, R. C.; Huerta, A. J.; McCool, P. M.; and Oshima, R. J. 1986. Response of beans to simulated ambient and uniform ozone distribution with equal peak concentrations. J. Am. Soc. Hort. Sci. 111:470-473.
Musselman, R. C.; Lefohn, A. S. ; Massman, W. J.; and Heath, R. L. 2006. A critical review and analysis of the use of exposure- and flux-based ozone indices for predicting vegetation effects. Atmos. Environ. 40:1869-1888.
Yamamoto, J.K. 2005. Comparing ordinary kriging interpolation variance and indicator kriging conditional variance for assessing uncertainties at unsampled locations, In: Application of Computers and Operations Research in the Mineral Industry – Dessureault, Ganguli, Kecojevic,& Dwyer editors, Balkema.