Contents:

• Introduction
• Data
  • PM10 Data
  • Temperature Data
  • Elevation Data
• Correction Factors
  • Pressure Correction
  • Temperature Correction
  • Combined Pressure and Temperature Correction
• Local Condition PM10
  • PM10 Correction Methodology
  • Quarterly Analysis
    • Temperature and Pressure Correction
  • Monthly Temperature Correction Analysis
• Conclusions
• Appendix A
• Appendix B

Introduction

Pollutant concentration data contained in EPA's Aerometric Information Retrieval System (AIRS) are required to be reported in units corrected to standard temperature and pressure (25 C, 760 mm Hg). The EPA is currently evaluating the clinical evidence to support retention of the requirement. This work analyzes the impact of a change from reporting PM10 concentrations at reference conditions to local temperature and pressure.

The influence of pressure and temperature individually on the correction was examined over a seasonal time scale. The two correction factors were then combined to produce the total correction facotr and subsequently, the PM10 concentration maps at local conditions. The temperature correction was further inspected for purposes of determining the difference between quarterly and monthly aggregations.

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Data

PM10 Data

The data used in this analysis were obtained from PM10 monitoring sites contained in EPA's Aerometric Information Retrieval System (AIRS). Daily (approximately every sixth day) sampled concentrations from 1995 were quarterly averaged. Quarter 1 contained data from January, February, and March; quarter 2 from April, May, and June; quarter 3 from July, August, and September; quarter 4 from October, November, and December. Sites were excluded from quarterly aggregation if they did not contain at least three valid data points. This filter ensured that a site had at least 20% of the total possible observations in a quarter. PM10 concentrations were also monthly averaged for January, July, and December. The monthly aggregation required at least two valid observations per month.

Temperature Data

Temperature data were obtained from the National Weather Service (NWS) Surface Trends data base. Data from 1992 was quarterly averaged and monthly averaged for January, July, and December. The aggregated temperatures were then interpolated to the AIRS PM10 monitoring locations. In the aggregation, only the local noon data were used. A spatial interpolation of 1/r² distance weighing of the nearest three sites was used. Figure 1 shows the locations of the temperature monitoring sites along with the contoured temperature for quarter 3. Figure 2 shows the NWS temperature data for all seasons. The squares associated with the site locations are proportional to the temperature measured at each location.

Figure 1. Quarter 3 temperature with stations overlaid.

Figure 2.Temperature monitoring sites.
Q1	Q2	Q3	Q4

Figure 3. NOAA Terrain Base Elevation Grid.

Correction Factors

The estimation of local condition PM10 concentrations (C_L) was achieved by multiplying PM10 concentrations at standard conditions (C_S) by a correction factor that was a function of the local temperature and pressure, f(P,T), as expressed by equation (2).

Pressure Correction

Pressure correction, defined as (P_L/P_S), is dependent upon a location's elevation. Since standard pressure is expressed at sea level and most locations in the U.S. (the exceptions being Death Valley and Imperial Valley in California) are above sea level, the local pressures will usually be equal to or less than 1 atmosphere and subsequently the pressure correction factor will be equal to or less than 1. As elevation increases, pressure decreases, thus expanding the volume of air and decreasing PM10 concentration at high elevation sites.

Figure 4 illustrates the pressure correction for the contiguous U.S. Over most of the U.S., the pressure correction factor is near unity. The mountainous regions of the west show the greatest amount pressure correction while the eastern half of the country, with the exception of the Appalachian Mountains and parts of New York state, has a correction factor of approximately 1.0. Much of the western US exhibits pressure decreases over 10% with the maximum (>25%) being reached over the Rocky Mountains in Colorado. The coastal region of the west has a correction factor near 1. The Imperial Valley of Southern California is represented in Figure 4 with a pressure correction greater than 1.

Figure 4. Pressure correction factor.

Figure 5.Quarterly temperature correction factors.
Q1	Q2	Q3	Q4

Figure 6.Quarterly pressure and temperature correction factor.
Q1	Q2	Q3	Q4

Figure 7.Quarterly pressure and temperature correction factor with stations overlaid.
Q1	Q2	Q3	Q4

Local Condition PM10

PM10 Correction Methodology
A flow diagram of the process used to generate the local condition PM10 maps is illustrated in Figure 8. The ovals in the figure represent the data which are the inputs/outputs of the data operators, represented by rectangles. Temperature and elevation data at AIRS station locations are retrieved from NWS temperature data and from the NOAA Terrain Base Elevation Grid. The temperature, elevation, as well as standard condition PM10 data for the AIRS locations are fed to the Local PM10 Operator which calculates the correction factors and corrects the PM10 concentrations to local conditions.

Figure 8. Flow diagram of the process used to generate the local condition PM10 maps.

Figure 9.PM10 concentrations at standard temperature and pressure.
Q1-1995	Q2-1995	Q3-1995	Q4-1995

Figure 10.PM10 concentrations at standard temperature and pressure with stations overlaid.
Q1-1995	Q2-1995	Q3-1995	Q4-1995

Figures 11 and 12 display PM10 concentrations corrected for local temperature and pressure. They show decreases in PM10 concentrations in the non coastal areas of the western US and relatively unchanged concentraiotns in the east. The difference between PM10 concentrations at local and standard conditions is presented in Figure 13. The largest decreases for each quarter were about 6 µg/m³ , or about 25%, and occurred in the Rocky Mountain area of Colorado. The eastern U.S. remained essentially unchanged during the spring and summer months with the locally corrected PM10 being within 2 µg/m³, or 5%, of the PM10 concentrations at standard conditions. Higher elevation areas such as the Appalachians and parts of New York state exhibited decreases between 2 and 4 µg/m³, or 5­10%. The winter months showed increases in PM10 concentrations in the Northeast and northern Midwest of about 10%. The coastal areas of the west coast were left unchanged by the correction to local conditions. The mountain region of the west displayed the largest decreases in PM10 concentrations during the spring and summer months.

Figure 11.PM10 concentrations corrected for local temperature and pressure.
Q1-1995	Q2-1995	Q3-1995	Q4-1995

Figure 12.PM10 concentrations corrected for local temperature and pressure with stations overlaid.
Q1-1995	Q2-1995	Q3-1995	Q4-1995

Figure 13.Difference between local condition PM10 and standard condition PM10.
Q1-1995	Q2-1995	Q3-1995	Q4-1995

Figure 14 contains annual average maps of standard condition PM10, local condition PM10, correction factor, and local PM10 to standard PM10 difference. These were derived by averaging the seasonal maps. They illustrate the concentration decreases in the mountain region of the west and the relatively unchanged concentrations of the east.

Figure 14.Annually averaged maps.
Standard Conditions	Local Conditions	Correction Factor	Local - Std. Difference

Figure 15.Quarterly scatter plots of eastern U.S. local PM10 vs. Standard condition PM10.
Q1	Q2	Q3	Q4

Figure 16.Quarterly scatter plots of western U.S. local condition PM10 vs. Standard condition PM10.
Q1	Q2	Q3	Q4

For purposes of quantifying the correction factor maps, frequency distributions were plotted for high elevation (> 500 m) and low elevation (<500 m) sites (Figure 17). Both the summer and winter quarters exhibit clear distinctions between low and high elevation sites. The low elevation sites have correction factors that are about or above 1.0. On the other hand, high elevation stations are characterized by correction factors less than 1.0. The seasons distinguish themselves in the magnitude of their correction factors. The winter average correction factor at high elevations is 0.91 while during the summer it is 0.85. At low elevations the average correction factor for the winter is 1.04 while for the summer it is 0.98. The summer experiences greater correction at high elevation locations because average temperatures are nearer to 25 C than they are during the winter which results in a temperature correction closer to 1.0. Thus, the decrease due to the pressure factor is not countered by temperature as much during the summer as it is during the winter.

Monthly Temperature Correction Analysis

The low influence of temperature on the correction of standard condition PM10 concentrations to local conditions was thought may have been the result of the quarterly aggregation. Averaging the temperature over three months may have overly smoothed the temperature variation. The correction to local PM10 was conducted for January, July and December and compared with quarter 1, quarter 3, and quarter 4 respectively.

Figure 18 displays scatter of monthly and quarterly derived temperature correction factors. The R2 for all of the plots are above 0.9 but the winter months show substantially more variation than the summer chart. The January and December averaged temperatures are colder than their respective quarterly averages resulting in higher temperature correction factors. The differences between the monthly and quarterly correction factor are generally less than 3% which indicates that using quarterly averaged temperatures is adequate for determining local condition correction factors.

Figure 18. Monthly correction factor vs. quarterly correction factor.

Figure 19. Frequency distributions and descriptive statistics for quarters 1, 3, 4 and January, July, December.

Figure 20. Temperature and pressure correction for Fairbanks, Alaska.

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Conclusions

The relation between pressure and temperature and local and standard conditions can be characterized through a seasonal analysis. Pressure plays the dominant role in areas of high elevation. Standard and local pressure are essentially equal in the eastern US and the coastal region of the west while the mountain area of the west and the Appalachians in the east have local pressures of 0.75 to 0.9 fraction of the standard pressure. Temperature exudes its greatest influence in the northeast and northern Midwest during the cold seasons with temperatures being up to 10% lower than the standard of 25 C. When combined, the western US is dominated by the influence of pressure while the northern portions of the east are influenced greater by temperature. The central Midwest and southeast are influenced by neither temperature nor pressure since local conditions are close to standard conditions.

The combined pressure and temperature correction of standard condition PM10 concentrations to local conditions is characterized by decreases in the mountainous western U.S. while the eastern U.S shows little correction. The western U.S. was characterized by decreases between 0.75-0.9 with quarter 3 having the greatest extent of the decreases in PM10 concentrations. Pressure is the dominant factor in determining the correction factor in the western US. The influence of temperature is greatest during the cold months (~10% increase in PM10 concentrations) over the Northeastern U.S.

The temperature correction factors produced by monthly aggregation did not substantially differ from those obtained using quarterly averaged temperatures. Quarterly correction factors therefore appear to adequately represent the relationship between PM10 concentrations at standard and local conditions.

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Appendix A

Comparison of AIRS Elevation with a Digital Model Elevation Grid

The elevations in the AIRS database were compared with a 2 km elevation grid. The AIRS site location latitudes and longitudes were used to extract elevations from the grid. The two elevations were then plotted against one another to produce Figure A1.

As illustrated in Figure A1, numerous sites do not display a 1:1 relationship between the two elevations. Noticeable is a linear correlation that is approximately 3:1, AIRS elevation to grid elevation. Investigation of these sites revealed that many of them had an AIRS elevation expressed in feet rather than meters. Other inconsistencies included numerous AIRS elevations of zero and a handful of AIRS sites with inexact latitude and longitude coordinates.

Corrections One hundred fifty three sites had elevations of zero. These values were replaced with the corresponding elevation grid value for the sites' respective locations.
The sites that appeared on the 3:1 line in Figure A1 were compared with their neighboring sites to confirm the assumption that their AIRS elevations were expressed in feet . The anomolous sites' elevations and PM10 concentrations were compared with those of surrounding stations. If a sites PM10 concentration was comparable to concentrations at nearby sites but the elevation was on the order of three times higher, the sites were corrected by converting their elevations from feet to meters. The sites whose elevations were converted from feet to meters are listed in Table A1.

Some sites had AIRS elevations much greater than its corresponding grid elevation. For these cases the elevation was examined for possible input error. For instance, Pomano Beach, Florida had an AIRS elevation near 700 meters. The corresponding grid elevation was about 6 meters. The neighboring sites to Pomano Beach had elevations around 10 meters so the AIRS elevation for Pomano Beach was changed to about 7 meters. Pomano Beach along with additional sites modified for elevation input error are listed in Table A2.

Table A2. Sites corrected for general elevation input errors.

Table A3. Sites corrected for incorrect latitudes and longitudes.

Figure 2A. Corrected AIRS to grid elevation scatter plot.

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Appendix B

An important data transformation processes used in this work is contouring. Most of the spatial patterns are presented on contour maps. The contours were derived from the point observations using a spatial interpolation scheme. In the first step, the data from the random locations were projected to a uniform grid that covers the conterminous U.S. The gridding used inverse distance squared (1/r²) as the station weighing factor. The interpolation outside the U.S. boundaries were trimmed to eliminate spurious values.
Contourer Pseudocode

Contour

Functionality: transform data from table format into grid format
Input: Table - a set of data points
WeightFunc - distance weight function
Radius - distance constraint
MinPoints - minimum required number of data points within Radius
MaxPoints - maximum number of data points used in calculation of a grid cell
Output: Grid - uniformly distributed set of data points
function Contour(out Grid, in Table, in WeightFunc, in Radius, in MinPoints, in MaxPoints)
{
for each Cell in Grid
CalcCell
}

CalcCell
Functionality: calculate value of a grid cell given a table of data points around it
function CalcCell(in out Cell, in Table, in WeightFunc, in Radius, in MinPoints, in MaxPoints)
{
PointList = an empty list of table points
for each point in Table that is not null
if the distance between the point and the cell is less than Radius then add point to the PointList, sorted by distance
Cell value = Interpolate PointList
}

Interpolate
Functionality: interpolate over a sorted list of data points
function Interpolate(in PointList, in WeightFunc, in MinPoints, in MaxPoints)
{
if number of points in PointList < MinPoints return null value
TotalWeight = 0
Sum = 0
WeightExp = 0 if WeightFunc = 1
1 if WeightFunc = 1/r
2 if WeightFunc = 1/r2, etc

for first MaxPoints in PointList
Dist = point distance from cell
Weight = 1 / (Dist^WeightExp)
TotalWeight = TotalWeight + Weight
Sum = Sum + Weight * point value
if TotalWeight = 0 return null value
Sum = Sum / TotalWeight
return Sum
}
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