For the 24-hour PM2.5 standard, a spatial field can be created using the high end of the

distribution of 24-hour PM2.5 concentrations. This is best represented by interpolating the

measured high values from each quarter. To be consistent with the attainment test, we

recommend interpolating the PM2.5 concentration in each quarter which is equal to or less than

the 98th percentile value for the year. For the PM2.5 component species, we recommend

interpolating the high PM2.5 days in each quarter. This can be based on the highest monitored

days in each quarter.

Interpolated data should be evaluated to quantify the errors associated with the

interpolation technique. Additional information on evaluation of spatial fields can be found in

(U.S. EPA, 2004c).

Step 2

The second step in the process involves the use of gridded model output to adjust the

spatial fields. The BenMAP software contains an example of this technique called eVNA. It

uses seasonal average model output data to adjust interpolated spatial fields (MATS also uses the

same technique). The eVNA technique has been used in health benefits assessments (U.S. EPA,

2005a). A specific metric is needed to determine the model predicted gradient in concentrations

for each of the NAAQS. For ozone, a logical metric is the 4th highest ozone prediction in each

grid cell23. For the annual PM2.5 NAAQS, the model predicted quarterly mean concentrations for

PM2.5 and PM2.5 species can be used to adjust the ambient spatial fields. For the 24-hour PM2.5

NAAQS, the gradient adjusted fields can be derived from the high end of the distribution of daily

averages in each quarter. This could be for all days with 24-hour predicted (or measured) PM2.5

> 65 ug/m3 (or some relatively high value) or simply the top 10% or 25% of all (PM2.5) days in

each quarter.


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